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Nothing too controversial for this week's discussion topic - but I wanted to share with you this short historical summary of Z-scores. It's interesting to note that the Z- test was originally used to measure errors in astronomical observations - and the normal curve was developed through coin flips (nothing remotely behavioural). Given what you now know about what "statistical significance" is and how the concept was arrived it - how do you feel about using statistics to make decisions about behaviour? Should Psychology change its approach?

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The origin of normal distribution can be traced to a French mathematician Abraham de Moivre. He had scientific interest in gambling and often acted as a consultant to gamblers to determine probabilities. De Moivre allegedly was studying the probability distribution of coin flips. He was trying to come up with a mathematical expression such as finding a probability of 60 or more tails out of one hundred coin flips. As an answer to this question he derived a bell shaped distribution which is commonly referred as the normal curve.

This was a crucial observation as a large number of phenomena follow approximately normal distribution. For example, such variables (phenomena) as height, weight and strength are characterized with normal distribution. That's why it is possible to determine one's weight or height standing compared to others using z score tables. A Belgian astronomer - Lambert Quetelet, was the first one who noticed the link between weight and height distribution and the normal curve.

Initially the normal curve was used to analyze the errors of measurement in astronomical observations. These errors happened due to instrument imprecision and observers' built-in bias.

It was Galileo who saw that errors were symmetric. He also observed that smaller errors were characterized by higher frequency. This gave an impetus to a chain of hypotheses about error distribution. However, it was only in nineteenth century that it was noted that these errors were normally distributed. Two mathematicians Adrian and Gauss developed formula for normal distribution independent of each other. Formula demonstrated that errors were well approximated by the normal curve.

It is worth noting that the same distribution was discovered by Laplace in late 18th century, when he developed very influential central limit theorem. According to this theorem, a

distribution of sample means (even if drawn from a non-normal distribution) follows the normal distribution. The larger the sample, the more the distribution approximates normal.

Normal distribution and related z score calculation have a wide application in a large number of fields ranging from social sciences to medicine. Z score is a useful standardized value which allows comparison among groups of people based on weight, height, test results, income and many other variables. If you need to find a z score use a z table here.

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The origin of normal distribution can be traced to a French mathematician Abraham de Moivre. He had scientific interest in gambling and often acted as a consultant to gamblers to determine probabilities. De Moivre allegedly was studying the probability distribution of coin flips.

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Since I was born, the amount of humans living in extreme poverty has decreased by 70%. However, since the pandemic hit in 2020, the downward trend of global poverty was halted, by an increase from 8.4% to 9.3% globally. Poverty is determined by percentage of people who live on less than $2.15 per day. Poverty is the opposite of prosperity and hence can be reduced if there is increase in properity, like by providing subsidies, free (basic) healthcare, free education upto grade 12 etc.

https://www.worldbank.org/en/topic/poverty/overview#1

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The World Bank Group's goals are to end extreme poverty and promote shared prosperity. This mission underpins our analytical, operational, and convening work in about 140 client countries.

For three decades, the number of people living in extreme poverty- defined as those who live on less than $2.15 per person per day at 2017 purchasing power parity-was declining. But the trend was interrupted in 2020, when poverty rose due to the disruption caused by the COVID-19 crisis. The number of people in extreme poverty rose by 70 million to more than 700 million people. The global extreme poverty rate reached 9.3 percent, up from 8.4 percent in 2019.

The world's poorest people bore the steepest costs of the pandemic. Their income losses were twice as high as the world's richest, and global inequality rose for the first time in decades. The poorest also faced large setbacks in health and education which, if left unaddressed by policy action, will have lasting consequences for their lifetime income prospects.

The recovery since then has been uneven. Rising food and energy prices-fueled in part by the war in Ukraine and by climate shocks and conflict-have hindered a swift recovery. By the end of 2022, as many as 685 million people could still be living in extreme poverty.

The recent setbacks took place when the speed of progress toward poverty reduction was already slowing, in tandem with subdued global economic growth.

The recent crises have pushed the world further off track from the global goal of ending extreme poverty by 2030. Given current trends, 574 million people-nearly 7 percent of the world's population-will still be living on less than $2.15 a day in 2030. And the challenge is made harder by the fact that extreme poverty is concentrated in parts of the world where it will be hardest to eradicate: in Sub-Saharan Africa, in conflict-affected areas, and in rural areas.

Last Updated: Nov 30, 2022

The COVID-19 pandemic dealt the biggest setback to global poverty in decades. The economic recovery has been uneven, affected by rising food and energy prices-due to the war in Ukraine and climate change, among other factors-and high indebtedness in many countries. In addition, extreme poverty is now more concentrated in places where it will be hardest to eradicate, including in conflict-affected and rural areas.

Strategies to reach the least well-off must be tailored to each country's context, considering the latest data and analysis, and the needs of the population. How the world responds to these major challenges today will have a direct bearing on whether the current reversals in global poverty reduction can be turned around.

The World Bank provides recommendations for a complementary two-track approach: responding effectively to the urgent crisis in the short run, while continuing to focus on foundational development problems, including conflict and climate change.
Closing the gaps between policy aspiration and attainment
Too often, there is a wide gap between policies as articulated and their attainment in practice-between what citizens rightfully expect, and what they experience daily. Policy aspirations can be laudable, but there is likely to be considerable variation in the extent to which they can be realized, and in which groups benefit from them. For example, at the local level, those who have the least influence in a community might not be able to access basic services. It is critical to forge implementation strategies that can rapidly and flexibly respond to close the gaps.
Enhancing learning, improving data
From information gathered in household surveys to pixels captured by satellite images, data can inform policies and spur economic activity, serving as a powerful weapon in the fight against poverty. More data is available today than ever before, yet its value is largely untapped. Data is also a double-edged sword, requiring a social contract that builds trust by protecting people against misuse and harm, and works toward equal access and representation.
Investing in preparedness and prevention
The COVID-19 pandemic demonstrated that years of progress in reducing poverty can quickly disappear when a crisis strikes. Prevention measures often have low political payoff, with little credit given for disasters averted. Over time, populations with no lived experience of calamity can become complacent, presuming that such risks have been eliminated or can readily be addressed if they happen. COVID-19, together with climate change and enduring conflicts, is reminding us of the importance of investing in preparedness and prevention measures comprehensively and proactively.
Expanding cooperation and coordination
Contributing to and maintaining public goods require extensive cooperation and coordination. This is crucial for promoting widespread learning and improving the data-driven foundations of policymaking. It is also important for forming a sense of shared solidarity during crises and ensuring that the difficult policy choices by officials are both trusted and trustworthy.

Overall, with more than 60 percent of the world's extreme poor living in middle-income countries, we cannot focus solely on low-income countries if we want to end extreme poverty. We need to focus on the poorest people, regardless of where they live, and work with countries at all income levels to invest in their well-being and their future.

The goal to end extreme poverty works hand in hand with the World Bank Group's goal to promote shared prosperity, focused on increasing income growth among the bottom 40 percent in every country. Boosting shared prosperity broadly translates into improving the welfare of the least well-off in each country and includes a strong emphasis on tackling persistent inequalities that keep people in poverty from generation to generation.

Our work at the World Bank Group is based on strong country-led programs to improve living conditions-to drive growth, raise median incomes, create jobs, fully incorporate women and young people into economies, address environmental and climate challenges, and support stronger, more stable economies for everyone.

We continue to work closely with countries to help them find the best ways to improve the lives of their least advantaged citizens.

Last Updated: Nov 30, 2022

The World Bank Group works to end poverty in several ways:

Funding projects that can have transformational impacts on communities.

Collecting and analyzing the critical data and evidence needed to target these programs to reach the poorest and most vulnerable.

Helping governments create more inclusive, effective policies that can benefit entire populations and lay the groundwork for prosperity for future generations.

Some examples:

Cambodia has achieved remarkable progress in reducing poverty and boosting shared prosperity, but key reforms are needed to sustain pro-poor growth. The World Bank is supporting Cambodia to help address the country's challenges of limited economic diversification, rapidly increasing urbanization, human capital deficiencies, and infrastructure gaps.

Mexico has experienced high income inequality and concentration of poverty in a few states. The World Bank Group has supported Mexico's efforts to develop a more inclusive, effective, and integrated social protection system including relaunching a conditional cash transfer program to help improve access to higher education and formal employment.

In one of India's poorest states, Bihar, a program financed by the World Bank has transformed livelihoods by mobilizing almost 10 million rural women into self-help groups and granting them access to finance and markets to start and expand their businesses.

A pilot program in Ecuador used text messages to relay information and encouragement to caregivers in an impoverished region of the country and saw a significant improvement in the nutrition and health of children.

Since 2007, a team of experts from the World Bank has been helping Kenya strengthen statistical capacity by reshaping its National Bureau of Statistics. With World Bank's support, the bureau implemented a range of surveys to update key indicators of official statistics and improved the data ecosystem. The project is funded by $50 million from the World Bank's International Development Association (IDA).

In-depth maps in countries such as Afghanistan, Bangladesh, Croatia, Republic of Serbia, and Vietnam show where economic diversity and gaps in services exist within a country. This, as part of the poverty assessment process, helps policymakers better target policies and programs to reach and benefit the poor.

Yemen's high malnutrition rates have drawn global attention, highlighting the impact the country's five-and-half-year civil war has had on its population. The Emergency Crisis Response Project gives pregnant women and women with children under the age of five money to buy food and teaches them about child nutrition. It has been able to reach more than 165,000 pregnant or lactating women and 175,000 children so far.

Conflict-affected communities in Mindanao are among the poorest in the Philippines, suffering from poor infrastructure and lack of basic services. The World Bank along with other partners have aimed to enhance access to services and economic opportunities and build social cohesion. These projects have help build water systems, community centers, sanitation facilities, access roads, post-harvest facilities, and farming and fishing equipment, benefiting 650,000 people in 284 villages in a decade.

An innovative series of rapid survey methodologies were pioneered in Somalia, one of the poorest countries in Sub-Saharan Africa. The surveys overcame significant security and implementation obstacles to yield the most comprehensive analysis of the welfare of the Somali people in decades and is now being used in other countries.

Last Updated: Nov 30, 2022

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Fighting poverty in all of its dimensions lies at the core of the World Bank's work. We work closely with governments to develop sound policies so that poor people can improve their livelihoods, and access social and infrastructure services and good jobs.

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Since I was born, the amount of humans living in extreme poverty has decreased by 70%. However, since the pandemic hit in 2020, the downward trend of global poverty was halted, by an increase from 8.4% to 9.3%. Poverty is determined by percentage of people who live on less than $2.15 per day. Poverty is the opposite of prosperity and hence can be reduced if there is increase in prosperity, like by providing subsidies, free (basic) healthcare, free education upto grade 12 etc.

https://www.worldbank.org/en/topic/poverty/overview#1

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Try out the following page made by Neal Agarwal and generate a couple of interesting statistics about you. Pick one statistic you find surprising and briefly report on it.

For myself - here was one telling statistic, life expectancy on average has gone up by "11" years on average since I was born. Lumping everyone into a single average here is actually a bit misleading because there are many factors that impact life expectancy, one of which is socio-economic status (Socioeconomic disparities in life and health expectancy among the household population in Canada (statcan.gc.ca), in particular education and income disparities dramatically reduce life expectancy through numerous factors: stress, diet, access to medical resources.

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Find out what has happened in your lifetime. How many times have you blinked? How much has the stock market gone up?

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I was born on September 22nd, 2003. Since then, the number of people living in extreme poverty has decreased by 65%. That sounds like quite a lot, which is awesome. We were on a steady poverty decline until Covid-19 happened. The United Nations reported that extreme poverty rates went from 8.4% of people in 2019 to 9.2% of people in 2020 because of the effects of the Covid-19 pandemic, with women and youths being disproportionately affected.  (https://unstats.un.org/sdgs/report/2021/goal-01/)

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Most people are aware of how good we are at making New Year's resolutions, and how bad we are at keeping them. Guilty! However, this 2011 article claims that 22% of Canadians who had set New Year's resolutions in the past managed to keep all of them! It frames the statement as if this were a small number, using the word "only," but I happen to find it quite large, and very surprising.

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Canadians Quick to Make New Year’s Resolutions, But Slow to See Them Through

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Eight in Ten (78%) Have Failed to Keep Past Resolutions; Just Two in Ten (22%) Canadians Have Managed to Keep All Their Resolutions in the Past

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Being a big crim. head myself, I have always been interested in seeing the statistics involving our legal/justice system, and/or how our government goes about dealing with these issues. With Indigenous people being so overrepresented in our criminal justice system, the statistics involving their treatment have always been some of the more jaw-dropping for me. An alarming one I came across recently was the fact that child welfare services and police reported that 16% of violence was experienced by Indigenous children, compared to 5.2% of violence experienced by non-Indigenous children. This is probably a direct effect of post-colonialism and the trickled-down effects of intergenerational trauma endured by Indigenous peoples.

https://www150.statcan.gc.ca/n1/pub/85-002-x/2022001/article/00012-eng.htm

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Victimization of First Nations people, Métis and Inuit in Canada

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First Nations, Métis and Inuit people are overrepresented among victims of violence; a violence that may be rooted in the traumatic history of colonialization. In efforts to better understand the causes, consequences, nature and extent of violent victimization of Indigenous people in Canada, this Ju…

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It is said that between 2020 and 2022, the number of accidental drug overdoses in BC has increased by 130%. For me, this percentage, which is above 100%, is very surprising and sad, while the government has used new methods of prevention.

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Since I was born, the world population has increased by over 2.9 billion people and 7% of those who were born at the same didn't make it to 2023. While it sounds fantastic since it means we have overcome many obstacles that human beings have struggled with throughout history such as life expectancy, birthrate, food and etc, it also alarming since our resources are limited and our environment does not have the capacity to host more people. Although, I have to admit that this issue is mainly related to modern consumer culture and how it facilitates and glorifies overconsumption through credits, ads and etc.

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Car accidents statistics in new year holidays

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Car accidents statistics in new year holidays

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The interesting statistic which I saw is about the number of car accidents which are happening around the new year in my country, Iran. It’s really a lot, and it’s really shocking that it’s happening every year and they still couldn’t find any way to stop this happening.

Tina Salasel

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Since I was born, the life expectancy went up 6 years for all people. I think this is because of the advanced health care system and healthier lifestyle. Despite, there are still a few regions where the life expectancy is very low that is because of less educated people and poor lifestyle. People have migrated to well developed countries in order to have a better lifestyle.

Jaspreet

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I was born may 28, 1999. I never knew the moon was moving away from us. i knew it fluctuated in distance. However i had no clue that i was slowly leaving our side. when  i read that The moon is 90 cm further away than when I was born, while its not a great distance, it took me completely by surprise. It makes me curious how the moon looked back when life was first becoming terrestrial, and also how it will look into another couple million years. would it look any bigger/smaller? and how would its greater distance to earth (be it further or closer) effect the oceans?

Jacob Dolan

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Since I was born there are over 3 billion more people on the planet. I find this very interesting because when I was born there were about 4.5 billion people. It’s is a huge increase over the last 40 years!

https://www.worldometers.info/world-population/world-population-by-year/

I also found it interesting that 9% of the people born the same year are no longer alive.

Becky Gillespie

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Bit of a morbid statistic, but I did find it surprising: I was born in 1997, and 6% of people born that year have since passed away. Perhaps I listen to too much true crime, and I wasn't expecting this to be a high number by any means, but I was a little surprised it was so low. So, I wanted to find out what demographic this statistic was referring to.

Although I wasn't able to find more information on this, I would suspect this number is based on demographics in North America. I would be curious to know more about the leading causes of death for 25-year-olds in this area, statistics on mortality rates of 25-year-olds worldwide, and how they correlate with factors such as socio-economic status, gender, etc.

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Have a look at the above tumblr "Bad Visualizations" which points out poor and misleading uses of graphs in the media. Pick one given on the site (or find one from a different site) and report on it. Provide the source of the graph

One that jumped out at me is this one Bad Visualisations — It’s downright dishonest to start bar graphs... (tumblr.com)
It gets a bit at what I was saying about representing data on the y-axis and truncating that axis (cutting off the 0). Only in select circumstances is this ok (when error bars are included representing how spread out the data is around the measure of central represented at the top of the bar). I would also note that the intervals of time given on the X-axis are unequal as well  .

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A collection of weird graphics and visualisations from here, there and everywhere. Don't follow if you love pie charts and are unwilling to change your mind about them

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Unfortunately as we all know, the cost of living has been steadily increasing over the years. From the website, I have learned that in 1999, $1.00 is equivalent to $1.78 today. That is close to being doubled! Even though the cost of living increased, the amount of people living in extreme poverty conditions has decreased by 70% since 1999.

Kaitlyn Baker

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One bad representation that caught my attention was the pie chart with sections that did not equal to 100%. When using a pie chart, the whole pie is 100%. So, the different sections of data should be equal to 100% when summed together.

Source: https://href.li/?https://www.espncricinfo.com/story/_/id/27143430/kane-williamson-hand-steadies-new-zealand-ship

Kaitlyn Baker

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Since i was born in 1989, the worldwide literacy rate has gone up from 82% to 87%. Doesn't seem like an overwhelmingly high jump but if we look at the actual number of people in the 5%, i am sure it must be a huge number! Also, not sure if it took into account 82% of the total population in 1989 vs 87% of the total population in 2023.. Perhaps the sample was taken from the total population in 1989 vs 2023 to get the difference.

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I found it interesting that since I was born, I have spent 2506 days of my life asleep because I have always wondered how much more I could do if I did not need to sleep. I understand that sleeping is essential and absolutely necessary for our body's health, but as I get older, I feel the time flying faster, and I cannot help but feel like I am wasting my time, especially when I am busy. Therefore, seeing the actual number of days I have spent sleeping makes me wonder how much of it was needed for my body and how much of it was wasted.

Canon Okubo

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$1.00 USD from when I was born is the equivalent of $4.98 USD today.
I'm guessing this is an average among all items but it sure would be interesting to see these comparison for independent segments... I wouldn't be surprised to see that technology is one of the segments with higher increases... Right?

Alejandro de Pablo

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It's amazing how by just playing with the scale of an axis you can absolutely skew the information of a graph. Unfortunately, this is a common practice used to deceive the intended public into what the perpetrator is trying to drive... So, the next time you analyze a graph make sure to fully understand what it is trying to demonstrate!

Alejandro de Pablo

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Didn't think I'm lucky to be alive now. 

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Didn't think I'm lucky to be alive now. 

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One of the interesting statistics about me is that 5%of the people born in 2002 didn't make it to now 2023.
I've always taken the fact that I'ma alive now for granted. This reminded me of the reality that there are people who couldn't survive up to now and were never given opportunities to design their lives!

Seowon Ryu

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The S&P500 is up 324% since you were born.

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The S&P500 is up 324% since you were born.

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At the current rate, the S&P500 has been increasing by 18% percent a year since 2004. I find it surprising that it's gone up this much, especially considering there's been a few very rough patches in the stock market (2008,2020).
- Matthew Bartos

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I've always never so cared about the intervals when reading these representations using a choropleth. It turns out it's quite important to see if all ranges have regular intervals. By not having regular intervals the data can be understood in a skewed, wrong way.

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Bet on the market, not individual stocks

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Bet on the market, not individual stocks

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For years, my savings have been sitting in a generic low-interest savings account. Over the last couple months, I've started to read quite a bit about investing. One piece of advice that I have constantly come across is to invest in EFTs and Index Funds rather than individual stocks. Outperforming the market is apparently quite difficult, especially over an extended period of time.

To think that the S&P 500 has increased over 1000% during my lifetime is absolutely mind boggling. If only I could go back in time and convince my 6-year old self to invest his allowance instead of buying Pokemon cards!

- Patrick Logan

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While the whole idea of using graphics is to clarify the data, too much visualization in the form of infinity amount of colour coding just adds to more confusion.

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There are 2,376,045,839 more people on Earth than when I was born in 1994. The world is becoming more competitive. Great minds are coming together and inventing commodities and services we never even felt we need them in the 90s. But there are many downside of growing population. Natural resources are depleting, demand for food, water, housing, healthcare are at it's highest with soaring cost of living. With increase in population, technology and comfort has increased rapidly. Unfortunately regeneration of natural resources is not as fast as tech development.

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Since the day I was born, a fact that brought joy to my day was that the average lifespan has increased by 7 years since the day I was born. To me this is surprising considering it seems like there is always more news and facts coming out about health scares in our society, and while it is easy to focus on the negative this is good news! I believe that the further we develop technology, we will develop more and more cures for things such as cancer, diabetes etc. I would love to know further details regarding this stat such as is crime factored into this? What is factored into this stat in order to determine it.

- Jenna

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This whole visualization of the baseball players averages is a great example of a poorly constructed visual. As mentioned in the description, there is little to no context on what the scores are representing such as the numbers in the red bubbles, as well as there is no representation of the scores along the scale which is misleading and confusing.

-jenna

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This graph is very difficult to read. The x axis does not have a consistent intervals of the years which skews how the data is represented. The data points are also very unclear as they are not displayed. The way colour is used to represent the data makes the graph very busy and unclear- there is too much going on here.

Source: Bank of America Merrill Lynch

Becky Gillespie

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Check out the X axis. It's really hard to find a pattern in terms of the scale that has been used. 1 and 1600 are as far apart as 1600 and 1913, and 1935 and 1953. It's insanity

I know what has happened - some bright analyst at BankAm Merrill Lynch made this in Excel. And instead of making an area chart (which is what it should be) they made some sort of a bar graph. So all the X axis data points they had got spaced evenly. And so resulted in absurdities like you see on this graph.

Source: Bank of America Merrill Lynch

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Check out the X axis. It's really hard to find a pattern in terms of the scale that has been used. 1 and 1600 are as far apart as 1600 and 1913, and 1935 and 1953. It's insanity I know what has happ...

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While a bit creative, using multiple different types of graphs at once is incredibly confusing. Beyond that both sets of bar graphs begin at different heights and are not consistent with the line graphs. overall this makes the information confusing to the interpreter. Should have just stuck with one style

- Jacob Dolan

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Too many series on one plot here. And I don't know why we have a mix of lines and bars - simple two-axis lines would've been good enough.

Also, the extra set of green bars to show growth is a bit confusing, especially since the high growth period happens at a time when the base was really really low.

And then the X axis labelling is atrocious (I guess this is Excel default). You don't need to label each tick.

I would possibly draw this graph with a logarithmic y axis. and dispense with all the bars.

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Too many series on one plot here. And I don't know why we have a mix of lines and bars - simple two-axis lines would've been good enough. Also, the extra set of green bars to show growth is a bit co...

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this one made laugh so hard because either someone really hated avocado toast or for some unknown reason he thought its a good idea to compare the housing prices between two different cities with amount of avocado toast would cost to buy them. Or something between those lines because I read it like 10 times and still don't know what its all about. It made me wonder how many houses I could buy with all the money I wasted on avocado toasts!
https://www.tumblr.com/badvisualisations/184827953341/this-is-not-all-you-need-to-click-through-to-this?source=share

Reza Bakhshi

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This is not all. You need to click through to this article on BBC, where you will find a few other such pictures. They use five pictures to convey the sort of information that one simple bar graph could have shown. And in dispersing this information across five graphs, they make it absolutely impossible to compare across these graphs.

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This is not all. You need to click through to this article on BBC, where you will find a few other such pictures. They use five pictures to convey the sort of information that one simple bar graph could have shown. And in dispersing this information across five graphs, they make it absolutely impossible to compare across these graphs.

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Since I was born, there have been 1,731,189,109 people born. I was a bit gobsmacked at this number. Considering my age, I didn't expect the number to be so high. I remember when the world population hit 7 billion people in 2011, and we've now hit 8 billion. I frequently hear that we're stressed out about the lower birth rates in developed countries, that I sometimes forget that it is not a universal experience around the world.

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This chart took up more brain power just trying to identify what each section was than the power needed to understand the content. It bothers me that you need to refer to the legend to figure out what each piece means. Continually looking back and forth also made me forget which label was for which colour. I also don't like how the percentages aren't right next to each piece of the chart. For some reason, the blue and red ones have their percentages right above, and the rest have theirs nowhere near the piece.

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Pie charts are bad enough. Half pies/doughnuts are worse - unless they're showing parliamentary majorities. This one does an atrocious job of labelling.

First of all you have the "tennis problem" where you need to keep looking back and forth from the chart to the legend to see what each segment represents. Then even the number are misplaced. I really don't understand why such long and wonky lines had to be drawn to place the numbers. And they're all so cluttered up.

I would've done this as a bar chart, but if a pie/doughnut/half-whatever was mandated, I would've still used direct labels on the chart rather than resorting to a separate legend. And the numbers have too many significant digits - they could be rounded off to the nearest percent without losing much information.

Source not clearly known. But I've seen this shared on social media by people from Zerodha and some other FinTechs

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Pie charts are bad enough. Half pies/doughnuts are worse - unless they're showing parliamentary majorities. This one does an atrocious job of labelling. First of all you have the "tennis problem" where you need to keep looking back and forth from the chart to the legend to see what each segment represents.

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At first I didn't understand if it's a graph or not. It's known that a graph always has 2 axis to define the variables. But this graph has no Y axis. Though in the graph it's represented as a 2 axis graph but it in unclear that what the Y axis specifies so therefore,  I think that there is no Y axis.

Jaspreet Kaur

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I spent a few minutes trying to figure out what the Y axis is here, and so far have no answers. Why would you distribute the points across two axes if the information is contained only in one? Sourc...

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Check out the above video "The Myth of Average" by Todd Rose - make sure to watch at least the first 5 minutes (the last part of the talk is great too though!). Todd discusses whether the "average" is a useful concept socially. He addresses an important question: are most people actually "LIKE" the average?  Tell me about an instance where you encountered something in the real world that was designed for the "average" person that didn't fit you very well (e.g., clothing, approaches to education, your car seat) - what do you think can be done to avoid the "flaw of averages"?

For me personally, I ran into the law of averages when I was in elementary school - and started covering courses in math. I remember not being able to "understand" it at the same rate that other kids were - and in particular had trouble grasping mental math. I remember they used to use these exercises called "mad minutes" (nice name) where you'd get a sheet with way more problems than you could possibly solve in a minute and you'd have to try and race through them and get as many done as possible, and I would struggle to get even one answered, while many of the other kids seemed quite good at it. To this day, I think about my math education and what could have been done to improve it, and all I can think is, maybe a bit more flexibility and compassion, and recognition that not everyone learns something like "addition" in the same way might have been nice. Of course I've learned to love math as an adult because of how useful it is at answering questions about research.

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The Myth of Average:  Todd Rose at TEDxSonomaCounty

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https://www.pedestrian.tv/news/herald-sun-misleading-pie-chart/

A great example of the bad visualizations of graphs/ charts that I see very often in the media is where the percentage and the graph/chart do not match like this. This is misleading the audience into thinking that "YES" is the opinion of the majority while taking advantage of the fact that many people do not pay attention enough to notice this error.

Canon Okubo

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Another day, another Murdoch newspaper publishing extremely questionable pie charts that have me wondering where on earth these people get the audacity. Do they think we won't notice?? First we had *that* ridiculous pie chart from The Australian. Now I introduce to you our latest culprit, the Herald Sun.

In the publication's April 24th newspaper, on page 11, a very curious (read: completely mind-boggling in its obvious meddling of facts) image can be found.

Probably not the only questionable graphic to ever appear in a Herald Sun newspaper, but certainly the most amusing: this totally accurate, not misleading at all graph that surely wholly represents the Australian public:

Alongside the question "Will Anthony Albanese having COVID-19 affect the Labor [Party's] chances at the election?", a graph shows "your verdict".

Apparently the poll found the majority of its audience voted no at 65 per cent. Yet for some reason that number is represented by the smaller segment of the pie chart?? The bigger segment represents the yes vote at only 35 per cent. Oh, and there's a typo in "Labor arty". What kind of trickery is this?!

Of course, maybe I shouldn't be surprised. It's not like misleading pie charts are exactly new for News Corp pubs.

But still, you'd think after all the other times they've been called out that they'd stop. Are you not embarazzed???

Twitter account @GraphRehab (lol) fixed the graph to show what it should actually look like.

There, I fixed it lol

Special thanks to https://t.co/7Aa0b3xFBT pic.twitter.com/KErfBWJLDh
- Graph Rehab (@GraphRehab) April 24, 2022

There we go! It's exactly the same but with the colours swapped around, as it should be.

I'm going to use this graph with my students this week to illustrate how not to use data
- 💧Sometime from the alternative left 🐀 🐙 (@Kristyg1) April 24, 2022

Now, is it possible that the graph designer just made a mistake and ~accidentally~ mislabelled the sides of the graph? Sure. Especially given the typo in its question, it's possible it was missed in the proof-read.

Weird how the mistakes never seem to flatter the Labor Party though, hey?

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Take A Look At This Herald Sun Pie Chart For A Lesson On How *Not* To Use Data

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Another day, another Murdoch newspaper publishing extremely questionable pie charts that have me wondering where on earth these people get the audacity. Do they think we won't notice?? First we had *that* ridiculous pie chart from The Australian. Now I introduce to you our latest culprit, the Herald Sun.

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This one from "Bad Visualizations" (photo link from twitter) really jumped out because it is so hard to read! The main problem is a lack of hierarchy; when there is a lot of information and all of it is treated with the same amount of importance, it makes it difficult for the reader to know where to begin or to identify an area they want to dig further into.

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The Family just got bigger #DisneyFoxDeal

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The Family just got bigger #DisneyFoxDeal https://t.co/4baeEx1PZk

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The Family just got bigger #DisneyFoxDeal pic.twitter.com/4baeEx1PZk
— Tyler Greenbacker (@TGreenback) March 20, 2019

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The Family just got bigger #DisneyFoxDeal https://t.co/4baeEx1PZk

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The Family just got bigger #DisneyFoxDeal https://t.co/4baeEx1PZk

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The Family just got bigger #DisneyFoxDeal

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I decided to go with this one, but I have seen many other of these that don't have any information in the Y axis. What kind of weird trend is that? I understand that it is meant to tell us about the biscuit market share percentage, but it's hard to tell anything about the placement of the lines when it does not list the percentage numbers, or anything frankly. Also, in regards to the X axis, although the range between the intervals match, what in the world is "YTD" written next to the last interval on the X axis? It makes it really hard to make any sense of what exactly is going on here. Taken from : https://www.tumblr.com/badvisualisations

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In design, "sizing" is done on the basis of ergonomic studies which average the measurements of the human body. As you can see in this example, the differences between the 5th, 50th, and 95th percentiles are considerable - and become painfully apparent when outliner consumers use a product designed for the mean.
To make things even more complicated, designers must accommodate not only based on these averages but, also, on the averages of weight, gender, ethnicity, etc... which makes finding the "perfect fit" an impossibility for most of us.

Alejandro de Pablo

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A graph from RT News taken from https://mathigon.org/task/fix-this-chart

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A graph from RT News that shows the Covid cases by day through the month of March in Russia. Everything seems reasonable at first glance. Judging by the bars, one might assume that Covid cases are either slowing down or leveling off. However, if you take a look at the numbers above each bar, you will start to notice that as the case counts increase drastically, the change in the height of the bars does not reflect this; they are not proportionate to the numbers that they represent.

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I was born in 2002. If you compare it to a dollar in 2002, the value of the dollar in 2023 is $1.65. The dollar has posted an annual average inflation rate of 2.41% since 2002, with a cumulative price increase of 64.98%.

-Daniel Minseok Kim

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Value of $1 from 2002 to 2023

$1 in 2002 is equivalent in purchasing power to about $1.65 today, an increase of $0.65 over 21 years. The dollar had an average inflation rate of 2.41% per year between 2002 and today, producing a cumulative price increase of 64.98%.

This means that today's prices are 1.65 times as high as average prices since 2002, according to the Bureau of Labor Statistics consumer price index. A dollar today only buys 60.606% of what it could buy back then.

The inflation rate in 2002 was 1.58%. The current inflation rate compared to last year is now 6.45%. If this number holds, $1 today will be equivalent in buying power to $1.06 next year. The current inflation rate page gives more detail on the latest inflation rates.

Annual Rate, the Bureau of Labor Statistics CPI

Buying power of $1 in 2002

This chart shows a calculation of buying power equivalence for $1 in 2002 (price index tracking began in 1635).

For example, if you started with $1, you would need to end with $1.65 in order to "adjust" for inflation (sometimes refered to as "beating inflation").

When $1 is equivalent to $1.65 over time, that means that the "real value" of a single U.S. dollar decreases over time. In other words, a dollar will pay for fewer items at the store.

This effect explains how inflation erodes the value of a dollar over time. By calculating the value in 2002 dollars, the chart below shows how $1 is worth less over 21 years.

According to the Bureau of Labor Statistics, each of these USD amounts below is equal in terms of what it could buy at the time:

This conversion table shows various other 2002 amounts in today's dollars, based on the 64.98% change in prices:

Inflation by City

Inflation can vary widely by city, even within the United States. Here's how some cities fared in 2002 to 2023 (figures shown are purchasing power equivalents of $1):

San Diego, California experienced the highest rate of inflation during the 21 years between 2002 and 2023 (3.57%).

Detroit, Michigan experienced the lowest rate of inflation during the 21 years between 2002 and 2023 (2.04%).

Note that some locations showing 0% inflation may have not yet reported latest data.

Inflation by Country

Inflation can also vary widely by country. For comparison, in the UK £1.00 in 2002 would be equivalent to £2.08 in 2023, an absolute change of £1.08 and a cumulative change of 108.14%.

In Canada, CA$1.00 in 2002 would be equivalent to CA$1.51 in 2023, an absolute change of CA$0.51 and a cumulative change of 51.36%.

Compare these numbers to the US's overall absolute change of $0.65 and total percent change of 64.98%.

Inflation by Spending Category

CPI is the weighted combination of many categories of spending that are tracked by the government. Breaking down these categories helps explain the main drivers behind price changes.

Between 2002 and 2023:

This chart shows the average rate of inflation for select CPI categories between 2002 and 2023.

Compare these values to the overall average of 2.41% per year:

The graph below compares inflation in categories of goods over time. Click on a category such as "Food" to toggle it on or off:

For all these visualizations, it's important to note that not all categories may have been tracked since 2002. This table and charts use the earliest available data for each category.

How to calculate inflation rate for $1 since 2002

Our calculations use the following inflation rate formula to calculate the change in value between 2002 and today:

Then plug in historical CPI values. The U.S. CPI was 179.9 in the year 2002 and 296.797 in 2023:

$1 in 2002 has the same "purchasing power" or "buying power" as $1.65 in 2023.

To get the total inflation rate for the 21 years between 2002 and 2023, we use the following formula:
Cumulative inflation rate (21 years)
Plugging in the values to this equation, we get:

Alternate Measurements of Inflation

There are multiple ways to measure inflation. Published rates of inflation will vary depending on methodology. The Consumer Price Index, used above, is the most common standard used globally.

Alternative measurements are sometimes used based on context and economic/political circumstances. Below are a few examples of alternative measurements.

Personal Consumption Expenditures (PCE) Inflation

The PCE Price Index is the U.S. Federal Reserve's preferred measure of inflation, compiled by the Bureau of Economic Analysis. It measures the change in prices of goods and services purchased by consumers.

The PCE Price Index changed by 2.11% per year on average between 2002 and 2023. The total PCE inflation between these dates was 51.96%. In 2002, PCE inflation was 1.31%.

This means that the PCE Index equates $1 in 2002 with $1.52 in 2023, a difference of $0.52. Compare this to the standard CPI measurement, which equates $1 with $1.65. The PCE measured -13.02% inflation compared to standard CPI.

For more information on the difference between PCE and CPI, see this analysis provided by the Bureau of Labor Statistics.

Core Inflation

Also of note is the Core CPI, which uses the standard CPI but omits the more volatile categories of food and energy.

Core inflation averaged 2.20% per year between 2002 and 2023 (vs all-CPI inflation of 2.41%), for an inflation total of 54.53%. In 2002, core inflation was 2.32%.

When using the core inflation measurement, $1 in 2002 is equivalent in buying power to $1.55 in 2023, a difference of $0.55. Recall that the converted amount is $1.65 when all items including food and energy are measured.

Chained Inflation

Chained CPI is an alternative measurement that takes into account how consumers adjust spending for similar items. Chained inflation averaged 2.21% per year between 2002 and 2023, a total inflation amount of 54.69%.

According to the Chained CPI measurement, $1 in 2002 is equal in buying power to $1.55 in 2023, a difference of $0.55 (versus a converted amount of $1.65/change of $0.65 for All Items).

In 2002, chained inflation was 1.26%.

Comparison to S&P 500 Index

The average inflation rate of 2.41% has a compounding effect between 2002 and 2023. As noted above, this yearly inflation rate compounds to produce an overall price difference of 64.98% over 21 years.

To help put this inflation into perspective, if we had invested $1 in the S&P 500 index in 2002, our investment would be worth approximately $4.91 in 2023. This is a return on investment of 390.74%, with an absolute return of $3.91 on top of the original $1.

These numbers are not inflation adjusted, so they are considered nominal. In order to evaluate the real return on our investment, we must calculate the return with inflation taken into account.

The compounding effect of inflation would account for 39.39% of returns ($1.93) during this period. This means the inflation-adjusted return of our $1 investment is $1.97. You may also want to account for capital gains tax, which would take your real return down to around $2 for most people.

Information displayed above may differ slightly from other S&P 500 calculators. Minor discrepancies can occur because we use the latest CPI data for inflation, annualized inflation numbers for previous years, and we compute S&P price and dividends from January of 2002 to latest available data for 2023 using average monthly close price.

For more details on the S&P 500 between 2002 and 2023, see the stock market returns calculator.

Data source & citation

Raw data for these calculations comes from the Bureau of Labor Statistics' (CPI), established in 1913. Inflation data from 1634 to 1912 is sourced from a historical study conducted by political science professor Robert Sahr at Oregon State University and from the American Antiquarian Society.

You may use the following MLA citation for this page: "$1 in 2002 → 2023 | Inflation Calculator." Official Inflation Data, Alioth Finance, 15 Jan. 2023, https://www.officialdata.org/us/inflation/2002?amount=1.

Special thanks to QuickChart for their chart image API, which is used for chart downloads.

in2013dollars.com is a reference website maintained by the Official Data Foundation.

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Inflation Rate between 2002-2023 | Inflation Calculator

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1 in 2002 is equivalent in purchasing power to about $1.65 today, an increase of $0.65 over 21 years. The dollar had an average inflation rate of 2.41% per year between 2002 and today, producing a cumulative price increase of 64.98%.

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In this graph, it is hard to know which axis explains what. People cannot know what the x-axis refers to and the y-axis refers to if there isn't any explanation under the graph. Without an explanation, it is just a picture with dots. Currently, I could not understand watching this graph with the country having a big belief in vaccines.

- Daniel Minseok Kim

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I spent a few minutes trying to figure out what the Y axis is here, and so far have no answers. Why would you distribute the points across two axes if the information is contained only in one? Sourc...

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The first thing that came to mind when thinking about averages in everyday life was clothing and shoes. To be more specific, jeans for clothing and tall boots for shoes.

Jeans because I need to buy the "short" length instead of the "normal" or "average" length because I am shorter.

Boots because I recently bought a pair of tall boots that are tighter around the calf and reach up to just below the knee. These boots JUST zipped up. If my calves were any bigger, the boots would have not fit. What is the "average" calf size for size 7.5 women's feet? Is there even an average for calf sizes compared to shoe sizes?! Most likely not.

Kaitlyn Baker

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Although the piano is a relatively recent instrument, it was still designed a long time ago and has not evolved much since. At the time, professional players were men (who tend to have larger hands than women) and the keyboard would have been designed for their average hand size. Anecdotally, we've all heard that piano players have long fingers! As a result, this makes playing the piano more challenging for people with smaller  hands. I never realized it could be an issue until hearing about this recent innovation that seems to be changing musical lives: a smaller keyboard! Sadly, as a small-handed person, I had stopped playing by the time I heard about it, but I'm glad to know that piano playing has become more inclusive.

- Annah Kassen

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The first time Monique Fournier sat down to play at a small piano keyboard, her fingers finally felt no pain.

The Calgary-based piano teacher and composer typically could play only 10 to 15 minutes on a standard grand piano before the strain forced her to stop.

Her hands are too small for a regular-sized piano keyboard. They barely stretch an octave.

But then she tried a small keyboard, modified to be 7/8ths the size of a regular one.

"It was one of those, 'Where has this been all my life?'" kind of moments," Fournier told the Calgary .

"I have so long wished that I could just stretch a tiny bit further, just have slightly bigger hands - or the piano could be smaller.

"Unfortunately, I couldn't do those first two things, so it was wonderful when the third one came to me."

Fournier has had her grand piano retrofitted to have the small-sized keyboard.

Instead of an octave being a six-and-a-half inch stretch, her new one is only five-and-a-half inches. She's no longer missing notes.

"It's much easier to reach," she said.

"The one thing I always notice when I go back to the regular keyboard after playing my small keys is that the keys look like giant marshmallows."

She shipped her grand piano to a U.S. business in the small city of Titusville, Penn.

The owner helped create the Donison-Steinbuhler Standard for pianists with smaller hands.

For $10,000, David Steinbuhler replaced her piano's keys with smaller ones. Other times, he replaces the action - the mechanism that holds the hammers that strike the piano strings - with a smaller one he made, as well.

"I'm wanting to encourage the world to adopt them. There is a profound discrimination," he said by phone from Titusville.

"Very often, it is life-changing for them."

Steinbuhler has sold at cost only 125 such adapted keyboards in 26 years.

One helped a piano student who was suffering carpal tunnel syndrome return to studying.

Steinbuhler started building the keyboard as a hobby after seeing one played in Niagara-on-the-Lake, Ont., in 1991.

Since then, he's developed two standard small sizes, and several universities are teaching with his product.

Now he's aiming to change his business into a non-profit to bring costs down.

Fournier says if anyone is interested in getting an adapted keyboard, she would be open to letting them try hers first.

She's holding a 7/8 keyboard demonstration at Michael Lipnicki Fine Pianos Ltd. on Friday at 7 p.m.
With files from Kathryn Marlow, Monty Kruger and the Calgary Eyeopener

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'Wonderful' keyboard innovation for small hands helps Calgary pianist play | CBC News

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Monique Fournier suffered strain whenever she tried to play a regular-sized piano keyboard, because her small hands barely stretch an octave. Now the Calgary-based piano teacher and composer has no trouble playing, thanks to an innovation that made her ask: "Where has this been all my life?"

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As a younger child and throughout my teens I always struggled with test anxiety, no matter how hard I studied i found the build up for a test would make me blank out and forget everything that I worked so hard to study for. I always found that most ways people test students is unfair as someone, like myself might know the information and be able to demonstrate it better in other ways but since writing tests is the most common way, It would reflect poorly throughout my academic journey. Luckily after years of practice I no longer get nervous for tests, unless its math then i still normally get worried.

Jenna McPhail

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Back when I was in my home country, Korea, I was a student who was one of the students that had the lowest height in the class. But all of a sudden, my height began to grow at a tremendous speed for a moment. Growing taller was good, but I found that my hands and feet grew faster and faster as I grew taller. The average size of an adult male in Korea is around US 9 shoes, and I have around 11 to 12 feet. Because of this, it was difficult to find shoes that fit my feet even in Korean stores or online shopping. However, after coming to Canada, my foot size became average here, which allowed me to buy shoes without stress here.

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I am 6'2. The average height for men in Canada is allegedly 5'10, and for women it is apparently 5'4 (which i struggle to believe). You can already imagine that this extra bit of height can make the world rather inconsistent and annoying to navigate.

The best examples that spring to mind exist in the bathroom. The sinks and mirrors are often to low and require some awkward crouching, but nothing will ever compare to the notorious shower. I'm sure many taller people find the same issues. the shower head is far to low in most showers. its like the scene from elf where he just needs to make it work the best he can.

An easy remedy would be to just have a detachable shower head, and i will credit whoever is designing these bathrooms, detachable heads are seemingly becoming more commonplace. However, that doesn't change the fact that its just generally awkward to navigate in most scenarios.

- Jacob Dolan

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The days I've spent sleeping is currently 2670. I find this somewhat hard to believe as this site most likely uses the average of hours that people sleep (7-9). I know that I have had times where my sleep hours have been abnormal. I would assume that my days slept are roughly 2670 but not an accurate time for 2670 days.

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The two lines used to signify their similarity fail to do so properly due to their design, the overlap fails to give a proper and accurate visual representation of their overlap. On the left side, the increments aren't consistent (0-10-30-50) and on the right side, they start at 160 and increase by 100s but aren't consistent in their visual distance.

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While growing up i never had much issues with clothing other finding .5 sizes of shoes but when I was about 17/18, a friend of mine had pointed out that I had broad shoulders and that the shirts I wear don't sit right on my body. Upon further investigation, they were right, the size of the shirts fit in accordance to my body size but it was apparent that my shoulders passed the shoulder length on these shirts. To combat this I simply just decided to dress in oversized tees or tees that matched my shoulder length rather than what was suppose to be my for my body size.

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I will never forget turning 12 and seeing all of the girls around me wearing clothes from Brandy Melville, a (still) very popular clothing store that has a "one size fits all" system for almost all of their pieces. I can assume that when they went about figuring out how to make the clothing, they would use an average size of what they thought their consumer demographic would be, but for me, they couldn't have been more off. I couldn't fit a majority of their clothes and at the time it sucked because pretty much all of my friends and virtually every girl around me was dressed in Brandy from head to toe. This company went on to face some backlash about their unconventional idea of "average size", (I was so relieved to realize it wasn't just me) which is why I think now I can actually fit in their clothes. Took them long enough!

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See the website above (storytelling with data) they challenged people to "visualize" variability in a variety of different ways. Pick one of the many submissions on this site and explain what variable they chose and how they visualized variability. What aspects of how they visualized variability made the variability in the dataset easy to understand? What made it challenging to understand?

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various views of variability — storytelling with data

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Wow, tons of variation in the ways people chose to display variability! Check out 41 visualizations of variability in data—featuring box plots, histograms, violins, reference bands and more—with topics ranging from weather to sports to love. Click to see the recap post that includes all the submissi

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I am around average height in Japan, so I  never had any problem growing up. However, ever since I came to Canada about five years ago, I have encountered many inconveniences, including the kitchen counter or sink being too high, not being able to reach high shelves or things at the store, clothes being too long, and people bumping into me because I am too short. Since I have heard my tall friends complaining about their problems, I understand that changing the height of things will not solve any problems. However, many brands can improve their size inclusivity regarding clothes.

Canon Okubo

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NBA Teams Points Scored - Spaghetti Plot

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NBA Teams Points Scored - Spaghetti Plot

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The visual representation I chose to interpret was Ben X who graphed the points scored for every NBA team for the 2018-2019 basketball season. He graphed these scores using a spaghetti plot. As you may know, there is a lot of NBA teams and even more scores for every game. So, this graph looks very messy. It actually looks like a child just scribbled all over the graph. However, they did include 2 lines that was one red and one blue. These different coloured lines represent the least and most variance which was helpful to visually see and interpret.

Kaitlyn Baker

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This visual representation was created to help convince high school staff that a certain level of math should be required to take physics. I find it effective at communicating the data, even to those with no knowledge of variability. Each dot represents a student score, the average scores are clearly stated, and the range of each group can be seen by the length of the line. It might be a bit harder to know the exact score of any particular dot because of it's distance from the y axis, but  this visualization seems more invested in the over arching message rather than individual scores.

Annah Kassen

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the more i learn about average the more i understand we have problem. this became big issue for me in my previous work which was commission based. they decided to cut down the commission pays by almost half and the excuse was that they want to pay more hourly and bring the pays close to average cause most people put the same effort but because their effort not always turns into sales it is unfair to them! I asked them how they came up with the average number cuz obviously that number wouldn't work for me and neither for my effort. they explained something that was Equivalent of mean for entire company which was weird because there were more outliers than expected working people because most of them were just hired and some of them were not even familiar with commission structure. to make it short I quit since i simply didn't find it fair. lastly I had the same issue with school in Iran in my elementary school since my grades were close lower than average but  later on when I grow up I realized the average number was so high and it wasn't based on average of students grades but it was based on average expected grades set by the institution!

Reza B.

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The average shoe size never fits me in width. Good thing they started making wide fit but because that's not the average shoe size, they come in poor design.

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This graph exemplifies my feelings for the Simpsons... A show that I used to enjoy very much. Variability here shows the overall success of each season and identifies how far apart the lowest and highest episodes were rated for each season... So, if you were interested in watching just one season, according to this graph your best bet would be season 3, the highest rated with lowest variability.

Alejandro de Pablo

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The visual representation that I chose was from Patrick where he tried to visualize the amount of rainfall in Milwaukee monthly from the year 2000 to 2018. Small blue clouds represent that, that month received less rainfall whereas the grey coloured clouds that those months in the specific year received a lot of rainfall. Though it's a great method to understand the variability of rainfall throughout the years but according to him Aug, 2007 received 12 times more rainfall than August, 2000. It is visible that in August 2007 there was higher precipitation as compared to August, 2000 but the number 12 times is not understandable as the only thing visible to us is the clouds and their colour. From that, I think no-one can guess that the rainfall was exactly 12 times more in 2007 August.

Jaspreet Kaur

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I liked this poster for two reasons: 1. it s easy to realize how big the sun is compared to other planets in the solar system. 2. It is also easy to compare the size of other planets to each other such as Venus to mars. However, the weak point of the poster is actually that! it perhaps provides too much information and while the main focus is the size of the sun compared to other planets it has way more information on it. The other problem with it is that the dots that compares the size of the planets to the sun doesn't really make sense. for instance, Jupiter is 10 times bigger than earth and 10 times smaller than the sun but the visuals for its size don't make sense! Reza Bakhshi

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A really good example of how outliers could possibly affect the mean and variance/standard deviation. Some publishers (e.g. Grand Central Publishing, Penguin Group, etc.) have a large amount of books that have been on the best sellers list, albeit for a short amount of time. Meanwhile, Riverhead Books has very few titles on the list but has one book that has been on the list for approximately 100 weeks.

Seeing everything graphically, in scale, helps me quickly understand how consistent each publisher is. If you gave me a number, it would be a lot harder to understand/interpret. Also, being able to compare different publishers side-by-side is much easier too. Again, comparing data between publishers visually is much more intuitive than doing it by numbers alone. However, as we saw in week 2, if the data isn't being properly displayed, people might be misled/come to a false conclusion.

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The Increasing Role of the Strikeout in MLB 

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The Increasing Role of the Strikeout in MLB 

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The graph that I chose was the increasing role of strikeout in MLB. This visual representation shows how since the 1910, how much the strikeout has been increased around the whole game. In this graph shows that the strikeout has been increasing gradually and show twice more of strikeout rate than the 1910.

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Hi again everyone. This week I have a chapter for you to read from a wonderful book by Curt Dudley-Marling and Alex Gurn. See the link to the PDF file above. It covers an important topic - to what degree does the "normal" curve provide us with an accurate representation for how human behaviour is distributed in the population. For your post this week, I want you to focus on the end message of this chapter. If the normal curve is a flawed representation of human behaviour - why do we continue to cling to it and what are some ways we can move past it (scientifically, socially, etc.).

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I chose this graph because frankly, it kind of confuses me. Here we are looking at the variability of fishing hours growth from the top 10 countries (not identified) from 2012-2017. There are many things I hate about this lined bar graph. First of all, which are the top 10 countries? And second, it makes it a little hard to be able to visually be able to tell which variables we want to look at. I can only get an idea of the actual numbers represented when they point out one of the notable findings and even then, they don't always help. Take the note starting with "September 2015: One country....." they noted a 35% increase in fishing hours, but not many numbers were stated and to be honest, I think the spikes being so far from the Y axis make it hard to visualize the numbers.

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I had chosen these graphs on cinema genres through history because it very easily showcases what genres were trending during their respective decades. It shows the increases and decreasing viewership of these three genres with added information explaining why some may have a spike in popularity or decline.

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https://at.tumblr.com/badvisualisations/1-as-a-rule-the-y-axis-of-a-bar-graph-needs-to/omsvhs2iaek6

This graph shows the average height of women from different countries. Except the graph is so poorly done. How can some women be 1/5 the size of others on average. Additonally, the y axis starts at around 5 foot. - Matthew B

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Thought these were interesting. not only does it provide us two different types of graphs to interpret, but it also does a great job showing us how the marriages/year vary. it does this well in the first graph by making use of the vertical axis while also comparing each state against eachother. the second graph grants us a more comprehensive comparison aswell.

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The bike industry offers sizes based on the XS-S-M-L-XL-XXL, in their own way. They usually offer a height range applicable to these sizes. But at the same time, every manufacturer has their own range for a specific size they offer. This has given rise to the bike fitting industry, and i can see the value in it. I have realized that as long as i get an XL, regardless of the manufacturer (i have had and have many bikes) it works for me, however, some L sizes have also been a good fit in the past.

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I like this graph, really enjoy astronomical stuff. I like the variable of distance from the sun, the terrestrial planets seem to be at distance in increments of 2x(Mercury-Venus) & 1.5x (Venus-Earth-Mars)which is very close to the actual distance (more or less) and so on. The sizing, i can understand is hard to depict very accurately without some moving graphics. They could have shown the relative size of Jupiter against the sun, once that is taken into account, then Saturn could be pitched against the size of Jupiter and so on so sort of get a better sense of the sizing of the planets.

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I think our reliance on the bell curve stems from its simplicity; it is easy to understand and can be readily used to give us an answer to any number of questions – even if those answers are wrong. In many ways, I think it has become a heuristic of sorts. A simple way to explain complex phenomena. The fact that the bell curve is used to explain data across a wide range of areas, lends it a sense of universality and, because of this, we are more likely to rely on it to explain behaviours – even when it is not appropriate to do so. Like any heuristic or cognitive bias, by learning about it, we are less likely to engage in it. So, just by reading this article, I would argue that we have all taken the first step in addressing this issue.

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As long as the experiments are valid and have been replicated with the same results we could be safe to assume that they are correct and, so, will probably be represented by a z score... right?

Alejandro de Pablo

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I think the statistics have been formed after conducting various experiments and studies and devising and rejecting various hypothesis and conclusions. Therefore, I believe statistics on behaviour make it easier to understand and interpret the data. For example, a scatterplot depicts the co-relation between two factors like age and occurrence of a disease. This gives us valuable insights on probability.

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I think that statistics is not always accurate in predicting every human behaviour.  But maybe that is because of the outliers that are present in the human population that will always remain.  Otherwise, I think that as experiments are conducted one after the other to prove that hypothesis to be true. So, it means that today's methods of predicting behaviour by statistical research are good.
Jaspreet Kaur

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I think that statistical significance can be somewhat misleading. It allows us to infer whether an observation or groups of observations have occurred by chance (e.g. natural variation) or not. However, it gives us no indication as to whether the observation(s) are "meaningful". While we might find something to be statistically significant, the differences between groups may be extremely small. Going back to the example in class today, while improvements in GRE scores after taking Doug's GRE course might be found to be statistically significant, an increase of 2 points on the GRE overall is not a "significant" increase. Like the general use of the word "theory" and "scientific theory", I think these terms can be easily conflated, especially by the wider public.

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I always wondered how these early theorist knew what they discovered was correct or valid. What if they just completely made something up, said it was true and we just believed it and continued to follow it? However, after reading the history and learning multiple people found the same pattern, it was mostly not made up. Even if it was made up, we are far too deep into using this system to turn back now.

Kaitlyn

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The next big topic in the class is hypothesis testing (specifically something called "null" hypothesis testing or NHST) in which we use the dreaded "p-value" to determine how "good" our results are. Blakely McShane and Andrew Gelman talk a bit about how in the social sciences (particularly Psychology) there is an underlying culture of thinking of p-values below .05 as "noteworthy" and those above .05 as "uninteresting" - this is an artificial dichotomy that's been imposed by decades of relying on this value as a publication threshold. For this topic, read the article linked above and tell me, what about "Null Hypothesis Testing" seems counterintuitive as a way to make decisions (are p-values hard to understand, and if so why?)? Is there anything mentioned in the article that you find shocking?

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As debate rumbles on about how and how much poor statistics is to blame for poor reproducibility, Nature asked influential statisticians to recommend one change to improve science. The common theme? The problem is not our maths, but ourselves.

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This is an interesting example of curiosity moving the field ahead; it's impressive what humans have achieved over time. Statistics (like many other things) can be a great tool for making decisions about behaviour, as long as we understand its strengths, limitations, and the context in which it is being used.
- Annah Kassen

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this has been my question since the beginning of the course. the result of this behavioral research is based on samples and on top of that the researcher has lots of options to manipulate their data such as the degree of freedom and error percentage( 0.01, 0.05 0.06). but every day I hear something on the news or tv that research has shown this and that. while I see the benefits of the current approach such as the production of a large amount of research and data, it is also concerning since the validity and reliability of this research is questionable. Thus, at least I think NEWS and media have to limit their publication to peer-reviewed articles and manipulated articles have to be mentioned where ever it gets publicized.   Reza B

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I like this quote: A crucial step is to move beyond the alchemy of binary statements about ‘an effect’ or ‘no effect’ with only a P value dividing them. Instead, researchers must accept uncertainty and embrace variation under different circumstances.
although I am aware that it may not be an easy option for most researchers since they have sponsored and invested lots of money in their research and producing an uncertain result after all that effort may damage their careers, however manipulating data or tests to produce desired results is also immoral. overall I think the current approach of predicting or analyzing human behavior and testing with mathematical formulas is wrong and we need a fundamental change in how and why we are doing research. Reza B

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I quite like the idea of preregistering analysis plans and sharing data. I think it would make things a lot more transparent. The turnover of research in academia is huge and when career prospects/funding/reputations rely so heavily on one's research, it's easy to understand why an academic may be tempted to cherry pick some data in order to make their research more attractive.

- Patrick Logan

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It never occurred to me how drastically researchers' (or corporations') access to data has changed and is affecting our interpretation of it. For the first time, we can know population averages in the billions in some areas (eg users of Facebook, credit cards etc.). Those numbers leave a lot of room for manipulation. Reading this article, it was shocking to me that preregistering analysis plans is NOT a requirement for research. It seems to be an easy leverage point that could keep research honest while still allowing room for exploration.

- Annah Kassen

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I believe a huge reason for why we cling to it, is the fact that is is the status quo, most of our methods and calculations are based around it in some fashion, and to move past it would mean venturing into something with no progress which is really difficult when you have something easy, even if flawed, right there.

Sometimes to move forwards, we need to take a few steps back and take a different path at the fork in the road.

- Jacob Dolan

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Yes it has a strange history, but the methodology is undeniable. While there could be improvements like with everything, all will come in due time as new minds apply themselves to the field of study.

As it stands statistics as it is gies us the opportunity to measure and make conclusions about large groups of people fairly accurately, even if that was an unintended use of it in its origin. However, much like many things in humanities history, we have a tendency to accidentally stumble upon things that work efficiently in other areas.

- Jacob Dolan

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What's so Significant about Significance Anyway?

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What's so Significant about Significance Anyway?

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We talk a lot about "significance" testing in this course and the importance of having a p-value that's less than 0.05 to claim it. In the next part of the course (covering t-tests) you're going to learn more about another class of statistics called "effect size" measures (e.g., Cohen's D). After reading the article, see if you can answer the following questions 1. How are effect size measures different from p-values? and 2. How are they connected to the idea of "sample size" (in what ways do sample size influence them)?

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Congratulations, your experiment has yielded significant results! You can be sure (well, 95% sure) that the independent variable influenced your dependent variable. I guess all you have left to do is write up your discussion and submit your results to a scholarly journal. Right............?

Obtaining significant results is a tremendous accomplishment in itself self but it does not tell the entire story behind your results. I want to take this time and discuss statistical significance, sample size, statistical power, and effect size, all of which have an enormous impact on how we interpret our results.

First and foremost, let's discuss statistical significance as it forms the cornerstone of inferential statistics. We'll discuss significance in the context of true experiments as it is the most relevant and easily understood. A true experiment is used to test a specific hypothesis(s) we have regarding the causal relationship between one or many variables. Specifically, we hypothesize that one or more variables (ie. independent variables) produce a change in another variable (ie. dependent variable). The change is our inferred causality. If you would like to learn more about the various research design types visit my article (LINK

For example, we want to test a hypothesis that an authoritative teaching style will produce higher test scores in students. In order to accurately test this hypothesis, we randomly select 2 groups of students that get randomly placed into one of two classrooms. One classroom is taught by an authoritarian teacher and one taught by an authoritative teacher. Throughout the semester, we collect all the test scores among all the classrooms. At the end of the year, we average all the scores to produce a grand average for each classroom. Let's assume the average test score for the authoritarian classroom was 80%, and the authoritative classroom was 88%. It would seem your hypothesis was correct, the students taught by the authoritative teacher scored on average 8% higher on their tests compared to the students taught by the authoritarian teacher. However, what if we ran this experiment 100 times, each time with different groups of students do you think we would obtain similar results? What is the likelihood that this effect of teaching style on student test scores occurred by chance or another latent (ie. unmeasured) variable? Last but not least, is 8% considered "high enough" to be that different from 80%?

Null Hypothesis: Assumed hypothesis which states there are no significant differences between groups. In our teaching style example, the null hypothesis would predict no differences between student test scores based on teaching styles.

Alternative or Research Hypothesis: Our original hypothesis which predicts the authoritative teaching style will produce the highest average student test scores.

Now that we have set the stage let's define what is a p-value and what it means for your results to be significant.

The p-value (also known as Alpha) is the probability that our Null Hypothesis is true. Obtaining a significant result simply means the p-value obtained by your statistical test was equal to or less than your alpha, which in most cases is 0.05.

A p-value of 0.05 is a common standard used in many areas of research.

A significant p-value (ie. less than 0.05) would indicate that there is a less than 5% chance that your null hypothesis is correct. If this is the case, we reject the null hypothesis, accept our alternative hypothesis, and determine the student test scores are significantly different from each other. Notice we didn't say the different teaching styles caused the significant differences in student test scores. The p-value only tells us whether or not the groups are different from each other, we need to make the inferential leap assume teaching styles influenced the groups to be different.

Another way of looking at a significant p-value is to consider the probability that if we run this experiment 100 times, we could expect at least 5 times the student test scores to be very similar to each other.

If we set our alpha to 0.01, we would need our resulting p-value is be equal to or less than 0.01 (ie. 1%) in order to consider our results significant. Of course, this would impose a stricter criterion and if found significant we would conclude there is a less than 1% chance the null hypothesis is correct.

The sample size or the number of participants in your study has an enormous influence on whether or not your results are significant. The larger the actual difference between the groups (ie. student test scores) the smaller of a sample we'll need to find a significant difference (ie. p ≤ 0.05). Theoretically, with can find a significant difference in most experiments with a large enough sample size. However, extremely large sample sizes require expensive studies and are extremely difficult to obtain.

Type I error (α) or false positives, the probability of concluding the groups are significantly different when in reality they are not. We are will to concede a 5% chance that we incorrectly reject the null hypothesis.

Type II error (β) or false negatives, is the probability of concluding the groups are not significantly different when in fact they are different. We can decrease the probability of committing a Type II error by making sure our statistical test has the appropriate amount of Power.

Power is defined as 1 - probability of type II error ( β). In other words, it is the probability of detecting a difference between the groups when the difference actually exists (ie. the probability of correctly rejecting the null hypothesis). Therefore, as we increase the power of a statistical test we increase its ability to detect a significant (ie. p ≤ 0.05) difference between the groups.

It is generally accepted we should aim for a power of 0.8 or greater.

Then we will have an 80% chance of finding a statistically significant difference. That said, we still have a 20% chance of not being able to detect an actual significant difference between the groups.

If you recall our teaching style example, we found significant differences between the two groups of teachers. The average authoritarian classroom test score 80% and the authoritative classroom was 88%. Effect size tries to answer the question of "Are these differences large enough to be meaningful despite being statistically significant?".

Effect size addresses the concept of "minimal important difference" which states that at a certain point a significant difference (ie p≤ 0.05) is so small that it wouldn't serve any benefits in the real world. Therefore, effect size tries to determine whether or not the 8% increase in student test scores between authoritative and authoritarian teachers is large enough to be considered important. Keep in mind, by small we do not mean a small p-value.

A different way to look at effect size is the quantitative measure of how much the IV affected the DV. A high effect size would indicate a very important result as the manipulation on the IV produced a large effect on the DV.

Effect size is typically expressed as Cohen's d. Cohen described a small effect = 0.2, medium effect size = 0.5 and large effect size = 0.8

Smaller p-values (0.05 and below) don't suggest the evidence of large or important effects, nor do high p-values (0.05+) imply insignificant importance and/or small effects. Given a large enough sample size, even very small effect sizes can produce significant p-values (0.05 and below). In other words, statistical significance explores the probability our results were due to chance and effect size explains the importance of our results.

We can calculate the minimum required sample size for our experiment to achieve a specific statistical power and effect size for our analysis. This analysis should be conducted a priori to actually conducting the experiment.

Power analysis is a critical procedure to conduct during the design phase of your study. This way you will have a good idea of the number of participants needed for each experiment group (including control) to find a significant difference(s) if there is one to be found.

G*Power is a great open-source program used to quickly calculate the required sample size based on your power and effect size parameters.

2. Select the "Statistical Test" you are using for your analysis

We will use Means: Difference between two independent means (two groups)

3. Select the "Type of Power Analysis"

We will select "A priori" to determine the required sample for the power and effect size you wish to achieve.

4. Select the number of tails

Use one tail if you only wish to determine a significant difference between groups in one direction. Typically, we select a 2-tailed test.

We will select a two-tailed test

5. Select the Desired Effect Size or " Effect size d"

we'll go through a range of effect sizes

6. Select "α erro prob" or Alpha or the probability of not rejecting the null hypothesis when there is an actual difference between the groups.

7. Select the power you wish to achieve.

We'll select 0.8 or 80% power and 0.9 or 90%

Select "Allocation Ratio N2/N1"

If you are expecting to have an equal number of participants in each group (treatment and control) then select 1. If you have twice as many in one group compared to the other group then select 2.

In general, large effect sizes require smaller sample sizes because they are "obvious" for the analysis to see/find. As we decrease in effect size we required larger sample sizes as smaller effect sizes are harder to find. This works in our favor as the larger the effect size the more important our results and fewer participants we need to recruit for our study.

Last but not least, these are the sample sizes requires for each participant group. For example, an experiment with one IV with 4 groups/levels and one DV, where you wish to find a large effect size (0.8+) with a power of 80%, you will need a sample size of 52 participants per group or 208 in total.

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The Relationship between Significance, Power, Sample Size & Effect Size

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Congratulations, your experiment has yielded significant results! You can be sure (well, 95% sure) that the independent variable influenced your dependent variable. I guess all you have left to do is write up your discussion and submit your results to a scholarly journal. Right............?

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1. p just measures the probability of an event happening while effect size determines the importance of said event.

2. p can sometimes be achieved with a large enough sample but that doesn't really mean that it is important - effect size helps us understand the real relevance of p

- Alejandro

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1. P-values measure whether there is statistical significance (probability) that the null hypothesis is correct. If the p-value is less than 5% we reject the null hypothesis because there is less than a 5% chance it is correct. The p-value only tells us there is a difference between the groups we are measuring and whether is was due to chance. Effect size essentially measures how much the IV affected the DV, and a high effect size demonstrates the IV had a large effect on the DV. It shows how important the effect of the IV was on the DV.

2. The sample size can influence the p-value. As sample size increases the p-value will reach statistical significance (p≤5%) but that does not mean there is significance. The effect size is not affected by the sample size because it is calculated by the data collected (independent of the sample size). This is why both p-value and effect size should be reported to illustrate the practical significance of the research findings.

Becky Gillespie

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the p-value doesn't reveal the effect size, however, it is a good indicator of an existing effect. increase or decrease in sample size affects p-value while effect size is independent and doesn't change with the size of our sample.
reza b

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1. P value is the probability that the null hypothesis is true. That is why the standard is usually set at 5% to ensure reliability. Effect size tells us if the p-value that we found is large enough to be really significant. we are looking for the p value to be as small as possible and effect size to be as high as possible for the alternative hypothesis to be accepted.

2. More the sample size, lesser the p-value. However, effect size is independent of sample size. Significance of effect size is to measure the effect of sample size on our findings.

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The p value is more about the probability of something happening or an event happening whereas the effect size tells us how important the event actually is.

The p value is the likelihood of us accepting the null hypothesis and has a 5% standard to make sure that this probability is reliable.

For effect size, we want the p value to be small and effect size to be large which allows us to (hopefully) reject the null and accept the alternative hypothesis

sadia rehman

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The p-value is the probability of obtaining a score assuming that null hypothesis is true. If p < a, this indicates that an effect is actually present and not due to error or variability. Another way to think of it is that the p-value represents the probability of making a Type-I error; rejecting the null hypothesis when it is actually true.

Effect size measures the difference in scores between two groups and can be used to infer a relationship between variables.

While effect size is not directly affected by sample size, p-values are.  As p-values are calculated using standard error (at least in t-tests), sample size (n) can influence them. This is problematic as the statistical significance of an effect can be determined arbitrarily by inflating one’s sample size. However, with that being said, it’s also important to have an adequate sample size because, as n increases, a sample will better represent the population (e.g. law of large numbers).
- Patrick Logan

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1. Effect size measures are different from p values in that they represent the impact that a IV has on a DV, rather than the statistical probability of results being obtained by chance. In a study rejecting the null hypothesis, we are generally looking for smaller p values and a larger effect size (usually described as Cohen's d).

2. A large enough sample size can usually find statistical significance, even if the effect size is low. This can result in a Type 1 error. On the other hand, if effect size is large enough, a small sample will easily represent it.

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This is a very relatable and interesting topic, and as I scroll through all of the different instances that people have encountered flaws in averages, the most common one for me is clothing. I find shopping for jeans wildly stressful because of height differences. So many brands have jeans that seem like they are build for unrealistically tall women, when the majority of average height is no where near the length. Therefore most jeans people buy end up needing to be hemmed.

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The visual representation i chose was the one I found to be most confusing and messy to look at which was the NBA team points scored on the graph that has what looks like scribbles outlining the game to game point variance between the Houston Rockets and the Dallas Mavericks. I found this graph very hard to read, it is very unclear and quite messy which made it almost impossible to fully understand.

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Statistics or experiments? Jenna McPhail

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Statistics or experiments? Jenna McPhail

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I feel that the current ways we measure behaviour in psychology, sometimes using statistics and sometimes using actual experiments go hand in hand together. In order for researchers to learn new things about people, I believe experiments are necessary in order to test new hypothesis, or even old ones to gain new or better understandings. I do trust many statistics but i think we need to incorporate both.

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1. Effect size measures differ from p values in that they depict the influence an IV has on a DV as opposed to the likelihood that results were attained by chance. We typically search for decreasing p values and a greater effect size (sometimes denoted as Cohen's d) in a study rejecting the null hypothesis.

2. Even if the effect size is small, a big enough sample size can typically detect statistical significance. This can lead to a Type 1 error. On the other hand, a small sample will accurately represent an effect size that is sufficiently large. - Matthew B

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Preregistering analysis plans and sharing data is a notion that I quite appreciate. It would, in my opinion, greatly increase transparency. It's understandable that an academic could be tempted to cherry-pick certain data to enhance their research given the enormous turnover of research in academia and the importance of one's research to career prospects, funding, and reputations.

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I believe that the present methods of measuring behaviour in psychology, which occasionally use statistics and occasionally use actual experiments, work well together. I think experiments are essential for testing new hypotheses or even old ones in order to get new or better understandings in order for researchers to discover new things about individuals. I do believe in many statistics, but I believe both should be used.

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Assuming were just meant to answer the prompt?

Significance is important because it lets us know when our numbers are actually... well... significant!

When we take a large group of people (or a sample) and we test them over the course of a few trials. their scores can add up in any number of ways.

The importance of p values and alpha are to indicate to us whether our variables reasonably effect things in a way that truly matters.

In the linked article, we see differences in study styles and academic performance. our p values and alpha will let us know if study styles significantly impact academic performances, much like we briefly covered in class.

- Jacob Dolan

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BMC Medical Education 18, Article number: 291 (2018) Cite this article

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Abstract

Background

Learning style refers to the unique ways an individual processes and retains new information and skills. In this study, we aimed to identify the learning styles of Turkish physiotherapy students and investigate the relationship between academic performance and learning style subscale scores in order to determine whether the learning styles of physiotherapy students could influence academic performance.

Methods

The learning styles of 184 physiotherapy students were determined using the Grasha-Riechmann Student Learning Style Scales. Cumulative grade point average was accepted as a measure of academic performance. The Kruskal-Wallis test was conducted to compare academic performance among the six learning style groups (Independent, Dependent, Competitive, Collaborative, Avoidant, and Participant).

Results

The most common learning style was Collaborative (34.8%). Academic performance was negatively correlated with Avoidant score ( p < 0.001, r = − 0.317) and positively correlated with Participant score ( p < 0.001, r = 0.400). The academic performance of the Participant learning style group was significantly higher than that of all the other groups ( p  < 0.003).

Conclusions

Although Turkish physiotherapy students most commonly exhibited a Collaborative learning style, the Participant learning style was associated with significantly higher academic performance. Teaching strategies that encourage more participant-style learning may be effective in increasing academic performance among Turkish physiotherapy students.

Peer Review reports

Background

Learning can be defined as permanent changes in behavior induced by life [ 1]. According to experiential learning theory, learning is "the process whereby knowledge is created through the transformation of experience" [ 2, 3].

Facilitating the learning process is the primary aim of teaching [ 4]. Understanding the learning behavior of students is considered to be a part of this process [ 5]. Therefore, the concept of learning styles has become a popular topic in recent literature, with many theories about learning styles put forward to better understand the dynamic process of learning [ 2, 3].

Learning style refers to an individual's preferred way of processing new information for efficient learning [ 6]. Rita Dunn described the concept of learning style as "a unique way developed by students when he/she was learning new and difficult knowledge" [ 7]. Learning style is about how students learn rather than what they learn [ 1]. The learning process is different for each individual; even in the same educational environment, learning does not occur in all students at the same level and quality [ 8]. Research has shown that individuals exhibit different approaches in the learning process and a single strategy or approach was unable to provide optimal learning conditions for all individuals [ 9]. This may be related to students' different backgrounds, strengths, weaknesses, interests, ambitions, levels of motivation, and approaches to studying [ 10]. To improve undergraduate education, educators should become more aware of these diverse approaches [ 11]. Learning styles may be useful to help students and educators understand how to improve the way they learn and teach, respectively.

Determining students' learning styles provides information about their specific preferences. Understanding learning styles can make it easier to create, modify, and develop more efficient curriculum and educational programs. It can also encourage students' participation in these programs and motivate them to gain professional knowledge [ 9]. Therefore, determining learning style is quite valuable in order to achieve more effective learning. Researching learning styles provides data on how students learn and find answers to questions [ 5].

Considering the potential problems encountered in the undergraduate education of physiotherapists, determining the learning style of physiotherapy students may enable the development of strategies to improve the learning process [ 12]. Studies on learning styles in the field of physiotherapy have mostly been conducted in developed countries such as Canada and Australia [ 13, 14]. A study conducted in Australia examined the learning styles of physiotherapy, occupational therapy, and speech pathology students. The results of this study suggest that optimal learning environment should also be taken into consideration while researching how students learn. The authors also stated that future research was needed to investigate correlations between learning styles, instructional methods, and the academic performance of students in the health professions [ 14].

To the best of our knowledge, there are no prior publications in the literature that report Turkish physiotherapy students' learning styles. Furthermore, previous studies mostly used Kolb's Learning Style Inventory (LSI), Marshall & Merritts' LSI, or Honey & Mumford's Learning Style Questionnaire (LSQ) to assess learning styles [ 5, 13, 15, 16, 17, 18]. Some of these studies also suggested that learning behavior and styles should be investigated using different inventories [ 5]. Moreover, a scale that was indicated as valid and reliable for Turkish population was needed to accurately determine the learning styles of Turkish physiotherapy students. Therefore, we opted to use the Grascha-Riechmann Learning Style Scales (GRLSS) to assess the learning styles of physiotherapy students, which will be a first in the literature.

Learning style preferences are influential in learning and academic achievement, and may explain how students learn [ 19]. Previous studies have demonstrated a close association between learning style and academic performance [ 20, 21]. Learning styles have been identified as predictors of academic performance and guides for curriculum design. The aim of this study was to determine whether learning style preferences of physiotherapy students could affect academic performance by identifying the learning styles of Turkish physiotherapy students and assessing the relationship between these learning styles and the students' academic performance. Since physiotherapy education mainly consists of practice lessons and clinical practice and mostly requires active student participation, we hypothesized that physiotherapy students with a Collaborative learning style according to the GRLSS would have higher academic performance.

Methods

A cross-sectional survey design using a convenience sample was used. The study population consisted of 488 physiotherapy students who were officially registered for the 2013-2014 academic year in Dokuz Eylul University (DEU) School of Physical Therapy and Rehabilitation. A minimum sample size of 68 participants was calculated with 95% confidence interval and 80% power by using "Epi Info Statcalc Version 6". Inclusion criteria were (i) age ≥ 17 years, (ii) official registration in DEU School of Physical Therapy and Rehabilitation for the 2013-2014 academic year, (iii) being a first-, second-, third-, or fourth-year undergraduate student of physiotherapy, (iv) ability to read, write, and understand Turkish, and (v) being willing and able to participate in the study. Exclusion criteria were (i) unwilling to participate in the study, (ii) inability to read, write, and understand Turkish. The questionnaire was distributed to the physiotherapy students in a classroom setting during the final exam week of the academic year. Due to the absence of participants who did not attend final exams and were not actively attending classes (non-attendance students), questionnaires were distributed to 217 students in total.

184 physiotherapy students with a mean ± SD age of 21.52 ± 1.75 years participated in the study. Participants were informed verbally and in writing about the purpose of the study and the survey that would be implemented. A research assistant was available in the classroom to provide assistance if required. Demographic characteristics (age, gender, undergraduate year) comprised the first section of the questionnaire, followed by the GRLSS to assess learning style.

Cumulative grade point average (CGPA) shown on the students' transcripts was used as the measure of academic performance. The students' CGPAs at the end of the 2013-2014 academic year were obtained from the records held in the student affairs unit of the DEU School of Physical Therapy and Rehabilitation. CGPA was derived by multiplying the grade point (out of 100) with the credit units for each module or course and then dividing the total sum by the total credit units taken in the program.

The local university ethics committee provided ethical approval and informed consent was obtained from the participants before inclusion. Ethical protocol number was 1432-GOA.

Data collection

Grasha-Riechmann student learning style scales

The GRLSS is a five-point Likert-type scale ( response format: strongly disagree, moderately disagree, undecided, moderately agree, strongly agree) consisting of 60 items which was designed based on student interviews and survey data [ 22, 23]. In accordance with the response to student attitudes toward learning, classroom activities, teachers and peers, six learning styles were defined [ 24]. Learning styles that form subscales are the Independent, Avoidant, Collaborative, Dependent, Competitive, and Participant learning styles [ 24, 25]. The six main styles in the GRLSS are described in Table 1 and the scoring of the GRLSS is shown in Table 2 [ 23, 24]. The GRLSS was adapted to Turkish in 2003 and found to have good reliability [ 25] (Table 3).

The learning styles of the physiotherapy students in the current study were identified according to GRLSS and the students were grouped based on their predominant (highest scoring) style. The mean and median academic performance values of each group were calculated and the significance of the differences between groups was statistically analyzed.

Statistical analysis

Statistical analyses were performed to compare academic performances among the learning style groups and test the significance of pairwise differences. All data were analyzed using Statistical Package for Social Science software (IBM Corporation, version 20.0 for Windows). Descriptive statistics were summarized as frequencies and percentages for categorical variables. Continuous variables were presented as mean and standard deviation when normally distributed and as median and interquartile range when not normally distributed. Mann-Whitney U test was used for between-group analyses of abnormally distributed variables. The variables were investigated using visual (histograms, probability plots) and analytical methods (Kolmogorov-Smirnov/Shapiro-Wilk test) to determine whether they showed normal distribution. As parameters were not normally distributed, the correlation coefficients and their significance were calculated using Spearman test. Strength of correlation was defined as very weak for r values between 0.00-0.19, weak for r values between 0.20-0.39, moderate for r values between 0.40-0.69, strong for r values between 0.70-0.89, and very strong for r values over 0.90 [ 26]. As the academic performance was not normally distributed, the Kruskal-Wallis test was conducted to compare this parameter among the six learning style groups. The Mann-Whitney U Test was performed to test the significance of pairwise differences using Bonferroni correction to adjust for multiple comparisons. An overall 5% type-I error level was used to infer statistical significance ( p  < 0.05).

Results

A total of 217 physiotherapy students were invited to participate in the study. Eighteen students refused to participate. Fifteen surveys were discarded due to missing item responses. As a result, data obtained from 184 students were used for the analyses. Overall response rate was 84.8%.

Demographic characteristics (gender, year) and learning style preferences are presented in Table 4. The most common learning styles among the physiotherapy students according to the GRLSS were Collaborative (34.8%) and Independent (22.3%). The results of GRLSS subscale scores were given in Table 5. The highest subscale score was Collaborative (Mean ± SD = 3.57 ± 0.62), while Competitive score was the lowest (Mean ± SD = 2.81 ± 0.69).

A moderate positive correlation between academic performance and Participant score was found (p < 0.001, r = 0.400). A weak negative correlation was also found between academic performance and Avoidant score (p < 0.001, r = − 0.317). No other significant correlation between academic performance and subscale scores was found (Table 6).

When students were grouped according to learning styles, between-group (Kruskal-Wallis) analysis showed a significant difference in the academic performance of the groups (p < 0.001). Post-hoc (Mann-Whitney U) analysis revealed significantly higher academic performance in the Participant learning style group compared to all of the other learning style groups (Independent, Avoidant, Collaborative, Dependent, and Competitive) (Table 7).

Discussion

The current study assessed the learning styles of Turkish physiotherapy students, and investigated the relationship between their learning styles and academic performance. The results revealed that the Collaborative learning style was most common among the Turkish physiotherapy students. However, students with Participant learning style had statistically higher academic performance when compared to the others. In addition, we found a positive correlation between Participant score and academic performance of the students, which supports the previous finding, while a negative correlation was found between Avoidant score and academic performance. In the case of physiotherapy students in this study, the emphasis should be on developing Participant and Collaborative learning skills. This might involve providing more class activities, discussions, and group projects.

The physiotherapy program at DEU has a combined case study-based and traditional style curriculum including lectures, tutorials, seminars, case study presentations, and supervised small group clinical practice in the hospital and at other health centers. Learning tasks and assessment methods include individual written examinations, practical examinations, homework and assignments as well as collaborative oral presentation and research projects. In the physiotherapy discipline, clinical practice improves students' occupational skills and is seen as a crucial part of the teaching process [ 12, 27]. Similarly, the teaching and learning approach at DEU is heavily based on practical training and requires active participation and group work. This could be a reason for the greater preference for Collaborative learning style.

Previous studies have indicated that physiotherapy students prefer abstract learning styles [ 28] and have desirable approaches to studying [ 29]. Canadian and American physiotherapy students preferred Converger (40 and 37% respectively) or Assimilator (35 and 28% respectively) learning styles [ 13]. According to descriptions of the learning style categories in the Kolb LSI, Convergers enjoy learning through activities like homework problems, computer simulations, field trips, and reports and demonstrations presented by others. On the other hand, Assimilators prefer attending lectures, reading textbooks, doing independent research and watching demonstrations by instructors when learning. In our study, Turkish physiotherapy students preferred Collaborative (34.8%) or Independent (22.3%) learning styles. According to GRLSS, Collaboratives prefer lectures with small group discussions and group projects (similar to Assimilators), while Independents prefer self-pace instruction and studying alone (similar to Convergers). Therefore, it can be concluded that learning styles of Canadian, American, and Turkish physiotherapy students are similar to each other.

Katz and Heimann used the Kolb LSI in their study and reported average learning style scores instead of the number of students in each of the four learning styles. They reported Converger as the "average" learning style for physiotherapy students [ 30]. In our study, the largest proportion of the physiotherapy students had a Collaborative learning style. Moreover, the average learning style was also Collaborative, with the highest average score.

Competitive learning style was the least frequently preferred (5.4%) by Turkish physiotherapy students in our study. The low preference for Competitive learning style indicates that students were less likely to compete with other students in the class to get a grade. Mountford et al. assessed learning styles of Australian physiotherapy students using Honey & Mumford's LSQ and found that the Pragmatic learning style was the least preferred. According to LSQ, Pragmatists tend to see problem solving as a chance to rise to a challenge [ 31]. Considering that both Competitives and Pragmatists like challenges, the least frequently preferred styles of Australian and Turkish physiotherapy students seem to be similar to each other.

Alsop and Ryan pointed out that "personal awareness of learning styles and confidence in communicating this are first steps to achieve an optimal learning environment" [ 32]. According to Kolb's theory, a preferred learning style affects a person's problem solving ability [ 13]. Wessel et al. also stated that in order to provide students the best learning opportunity, educators must be aware of the learning styles and students' ability to solve problems [ 13]. Indeed, evidence supporting these views can be found in the literature. Previous studies showed that students who were aware of their learning style had improved academic performance [ 33, 34]. Nelson et al. found that college students who were tested on their learning style and were given appropriate education according to their learning style profile achieved higher academic performance than other students [ 33]. Linares also investigated learning styles in different health care professions (physiotherapy, occupational therapy, physician assistants, nursing and medical technology) and found a significant relationship between learning style and students' readiness to undertake self-directed learning [ 15]. However, Hess et al. found no association between learning style and problem-solving ability in their study [ 35].

While planning this study, we hypothesized that students with a Collaborative learning style would have higher academic performance. Although the Collaborative learning style was the most common, these students did not show significantly higher academic performance. However, students with Participant learning style had statistically higher academic performance when compared to the other learning style groups. Characteristics specific to the Participant learning style are enjoyment from attending and participating in class and interest in class activities and discussions. These students enjoy opportunities to discuss class materials and readings. This may suggest that increasing in-class activities and discussions, which encourage participant-style learning, is needed to increase academic performance. Another approach would be to adapt teaching strategies according to the characteristics of Collaboratives, as they represented the largest body of students. Creating a convenient environment in which students could spend more time sharing and cooperating with their teacher and peers may facilitate collaborative learning, thus enhancing academic performance. Organizing the curriculum to include small group discussions within lectures and incorporate group projects may also be beneficial. As Ford et al. stated, " Identification teaching profiles could be used to tailor the collaborative structure and content delivery" [ 36].

The most important reason for determining learning style is to create a proper teaching strategy [ 37, 38, 39, 40]. However, there seems to be no exact relationship between students' learning style and the curriculum of a program described in the literature [ 13]. Learning style alone is not the only factor that may influence a learning situation. Many factors (educational and cultural context of university, individual awareness, life experience, other learning skills, effect of educator, motivation, etc.) may influence the learning process [ 31]. Therefore, expecting a simple relationship between learning style and teaching strategy may not be realistic. Moreover, the review of Pashler et al. showed that there was virtually no evidence that people learn better when teaching style is tailored to match students' preferred learning style [ 41]. Nevertheless, future studies investigating physiotherapy educators' teaching styles and their association with learning styles and academic performance may elucidate this complex issue.

The major strength of this study is that, to the best of our knowledge, ours is the first study investigating the learning styles of Turkish physiotherapy students with relation to academic performance.

There were some limitations to this study. It should be noted that learning style is a self-reported measure that can change based on experience and the demands of a situation. Therefore, it is subjective and able to provide adaptive behavior [ 42]. It should also be kept in mind that the conclusions of this study could be limited due to the cross-sectional design, and respondent bias may be an issue because convenience sampling was used to recruit participants. One possible limitation of the study could be the fact that the three of the scale reliabilities reported for GRLSS was poor.

This study investigated the learning styles of physiotherapy students in only one university (DEU) and this could preclude the generalization of our results. Subsequent studies should include students enrolled in the physiotherapy departments of multiple universities in Turkey to achieve an accurate geographical representation. Moreover, future studies on this topic should be conducted in collaboration with universities in Europe, with which we share a cultural connection.

Conclusions

The results of this study showed that the Collaborative learning style was most common among Turkish physiotherapy students. On the other hand, the physiotherapy students with Participant learning style had significantly higher academic performance than students with other learning styles. Teaching strategies consistent with the unique characteristics of the Participant learning style may be an effective way to increase academic performance of Turkish physiotherapy students. Incorporating more in-class activities and discussions about class material and readings may facilitate Participant learning, thus impacting academic performance positively. Another approach may be to adopt teaching strategies that target the predominant Collaborative learning style. Creating a convenient environment for students to share and cooperate with their teacher and peers and organizing the curriculum to include more small group discussions and group projects may also be supportive. Future studies should investigate physiotherapy educators' teaching styles and their relations with learning styles and academic performance.

Abbreviations

CGPA:

Cumulative Grade Point Average

DEU:

Dokuz Eylul University

GRLSS:

Grascha-Riechmann Learning Style Scales

LSI:

Learning Style Inventory

LSQ:

Learning Style Questionnaire

References

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Acknowledgements

The authors like to thank all physiotherapy students who participated in this study.

Funding

No funding was obtained for this study.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Author information

Authors and Affiliations

School of Physical Therapy and Rehabilitation, Dokuz Eylul University, 35340, Inciralti, Izmir, Turkey

Nursen İlçin, Murat Tomruk, Sevgi Sevi Yeşilyaprak, Didem Karadibak & Sema Savcı

Contributions

Nİ conducted the literature search for the background of the study, analyzed and interpreted statistical data, and wrote the majority of the article. MT conducted the literature search, collected data for the study, analyzed statistical data, and contributed to writing the article. SSY and DK were involved in study planning, data processing, and revising the article. SS contributed to study design and oversaw the study. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Nursen İlçin.

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Author's information

Nursen İlçin, PT, PhD.

İlçin graduated from Dokuz Eylul University, School of Physical Therapy and Rehabilitation in 1998. She received her Master's degree in 2002 and PhD in 2009 from Dokuz Eylül University. She is currently a associate professor in Geriatric Physiotherapy Department.

Murat Tomruk, PT, PhD.

Tomruk graduated from the School of Physical Therapy and Rehabilitation at Dokuz Eylul University in 2009. He received his MSci degree in Musculoskeletal Physiotherapy in 2013 and his PhD degree in 2018. His doctorate thesis was about manual therapy. He works as a research assistant at Dokuz Eylul University since 2011.

Sevgi Sevi Yeşilyaprak, PT, PhD.

Sevgi Sevi Yeşilyaprak's speciality is shoulder rehabilitation. Her primary research interests are orthopaedic and sports injuries of the shoulder, shoulder biomechanics, proprioception, and exercise. She has one active and two completed grants. Yeşilyaprak teaches courses on musculoskeletal physiotherapy including sports physiotherapy, musculoskeletal disorders, therapeutic exercises, exercise prescription, and manual physiotherapy techniques.

Didem Karadibak, PT, PhD.

Karadibak obtained her BS degree in Physiotherapy from Hacettepe University in 1992 and her MS and PhD degrees from the Physical Therapy Program of the Institute of Health and Sciences, Dokuz Eylul University in 1998 and 2003, respectively. She is currently a professor of Cardiopulmonary Rehabilitation in the Dokuz Eylul University School of Physical Therapy and Rehabilitation.

Sema Savcı, PT, PhD.

Savcı obtained her BS degree in Physiotherapy from Hacettepe University in 1988 and her MS and PhD degrees from the Physical Therapy Program of the Institute of Health and Sciences, Hacettepe University in 1990 and 1995, respectively. She is currently a professor and serving as the Head of Cardiopulmonary Rehabilitation in the Dokuz Eylul University School of Physical Therapy and Rehabilitation.

Ethics approval and consent to participate

Written ethical approval was taken from the Dokuz Eylül University's local ethics committee (approval number 1432-GOA) and written informed consent obtained from all the participants.

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Not applicable.

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The authors declare that they have no competing interests.

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İlçin, N., Tomruk, M., Yeşilyaprak, S.S. et al. The relationship between learning styles and academic performance in TURKISH physiotherapy students. BMC Med Educ 18, 291 (2018). https://doi.org/10.1186/s12909-018-1400-2
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DOI: https://doi.org/10.1186/s12909-018-1400-2

Keywords

Learning style

Academic performance

Physiotherapy

Student

Turkish

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The relationship between learning styles and academic performance in TURKISH physiotherapy students - BMC Medical Education

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Background Learning style refers to the unique ways an individual processes and retains new information and skills. In this study, we aimed to identify the learning styles of Turkish physiotherapy students and investigate the relationship between academic performance and learning style subscale scores in order to determine whether the learning styles of physiotherapy students could influence academic performance.

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Perhaps the p value is a little more confusing than the average statistician would think.

When we first brought up p values and significance there were questions of what it means to be insignificant vs significant. Moreover, I brought this into my daily like and had asked some friends and family (much to their dismay)

It appears that when something isnt significant, it is deemed as unimportant or worthless, and that only the significant values truly matter. I believe this comes down to the cultural understanding of what significant actually means, vs how we use it in statistics. Much like how negative and positive so not indicate good and bad, significance does not deem worth so much as level of impact so to say. that being said insignificant scores are not worthless, just not incredibly impactful

- Jacob Dolan