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Made by: Dhruvil Patel, working individually.
Student ID: 100683668

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Post #2 Red Flag: Appeal to Authority (video)

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Post #2 Red Flag: Appeal to Authority (video)

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Definition: Appeal to Authority means using a public figure like a famous celebrity, athlete or a personality who has a huge following to advertise a product. It is the appearance of the celebrity being used to promote the product in order to reach to a larger audience.
Connection: The video below shows the promotion of Star Value Pack which is being advertised by MS Dhoni and Hardik Pandya who are famous Indian cricketers. Cricket is a very famous sport in India and cricketers are hugely praised and even considered as Gods. Therefore, casting cricketers in promoting their product is a great marketing strategy

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#SachMein Hardik Pandya, Mahendra Singh Dhoni? #StarValuePack

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Definition: The red flag ancient wisdom includes anything that gives you a clue about ancient beliefs, theories and also about their knowledge of the world which are mostly proven wrong by science and observations. It is not logical to think that they were more skillful and knowledgeable than us. For example, thinking that earth is flat.
Connection: The link attached below shows six ancient beliefs that are yet to be proven true.

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The idea that "rain follows the plow" was considered valid until the 1890s, at which point severe droughts arrived and people put two and two together. Still, hardcore proponents of the theory insisted that the rain had stopped only because the settlers weren't plowing deep enough. Which, naturally, is the only logical explanation.

Most medieval doctors had no idea what was going on or what to do about it, so they would pull cures out of their asses -- literally. In medieval times, cures for diseases often called for the liberal application of shit. Anglo-Saxon medicine implemented the excrement of goats, sheep, horses, calves, oxen, and pigeons, mixed with different household ingredients. One headache cure, for example, called for a recipe of pigeon dung, incense, wheat flour, and egg white. Which are, incidentally, the exact same ingredients you'll find in a Cadbury Creme Egg.

The "Throw poo at it" approach remained a medical option in various forms long after the middle ages. 17th-century chemist Robert Boyle preached a treatment for cataracts which required human excrement to be dried into powder and then blown into the eye. An 18th-century minister promoted an epilepsy cure which urged the patient to swallow pulverized baby poop over the course of several days. Medieval medicine was an elaborate Jackass-style prank, with the reveal coming centuries after everyone had died from hardcore pinkeye.

In the 1600s-1700s, the scientific and religious communities (not entirely separate) widely believed in the concept of "preformation," which stated, essentially, that all species had been made by God at the time of Creation, and animals "growing" was simply the unfolding of preformed parts. So every creature, including humans, were biological Russian nesting dolls. Oddly enough, they were kind of right about people coming from tiny reproductive cells located in the parents, buuuut they fudged the specifics a wee bit, believing instead that a tiny intact human was chilling inside of a sperm cell .

The idea was that humans came to life when the minuscule adult got sick of chillaxing and decided to enlarge. Scientists could not agree if the teeny person was located in the sperm or the ovum. Looking back today, it doesn't matter which was accepted, because both work equally well for our purposes: laughing at old-timey idiots.
Think Nana and Pop-Pop's loving 60-year monogamous relationship is quaint and old-fashioned? First off, sorry for that disturbing image, but we've got some news for you: the monogamous sexual relationship is actually brand new relative to how long humans have been around. Secondly, it's about to get worse from here: monkey sex. On this month's live podcast, Jack O'Brien and the Cracked staff welcome Dr. Christopher Ryan, podcaster and author of 'Sex at Dawn', onto the show for a lively Valentine's Day discussion about love, sex, why our genitals are where they are, and why we're more like chimps and bonobos than you think. Get your tickets here: For more crazy frightening things about our ancestors, check out The 10 Most Insane Medical Practices In History and 8 Terrifying Instruments Old-Time Doctors Used On Your Junk. Subscribe to our YouTube channel, and check out 4 Awful Ways Our Ancestors Got High (That We Tested!), and other videos you won't see on the site! Follow us on Facebook, and let's be best friends forever.

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6 Extremely Wrong (But Hilarious) Scientific Beliefs

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The idea that "rain follows the plow" was considered valid until the 1890s, at which point severe droughts arrived and people put two and two together. Still, hardcore proponents of the theory insisted that the rain had stopped only because the settlers weren't plowing deep enough. Which, naturally, is the only logical explanation.

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Post #4 Red Flag: Confirmation Bias (video)

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Post #4 Red Flag: Confirmation Bias (video)

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Definition: Confirmation Bias means that we usually believe the things that we see or the things that our mind wants us to see or hear and ignoring the fact that our mind doesn't believe it.
Connection: The video linked below shows a magician making people believe that his tricks are real. The audience seem to be convinced that his tricks are real.

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Dynamo Magic Impossible: The New Amazing Magic 2018

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Post #5 Red Flag: Confusion of Correlation with Causation (link)

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Post #5 Red Flag: Confusion of Correlation with Causation (link)

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Definition: Confusion of Correlation with Causation  is based on an ancient myth that an action always has a result even if the result is not related to the action at all. For example, keeping a shoe upside down can lead to internal dispute among the housemates.
Connection: The link attached contains some similar ancient myths that are ridiculous.

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Because the human brain tends to seek out causal relationships, scientists are extra careful about creating highly controlled experiments - but they still make mistakes. Here are ten examples illustrating how hard it is to identify causation.

10. The Trouble With Henry (and Hawthorne)

People are a pain to research. They react not only to the stimulus being studied, but also to the experiment itself. Researchers today try to design experiments to control for such factors, but that wasn't always the case.

Take the Hawthorne Works in Cicero, Illinois. In a series of experiments from 1924 to 1932, researchers studied worker productivity effects associated with altering the Illinois factory's environment, including changing light levels, tidying up the place and moving workstations around.

Just when they thought they were on to something, they noticed a problem: The observed increases in productivity dropped almost as soon as the researchers left the works, indicating that the workers' knowledge of the experiment - not the researchers' changes - had fueled the boost. Researchers still call this phenomenon the Hawthorne Effect [source: Obrenović].

A related concept, the John Henry effect, occurs when members of a control group try to beat the experimental group by kicking their efforts into overdrive. They need not know about the experiment; they need only see one group receive new tools or additional instruction. Like the steel-driving man of legend, they want to prove their capabilities and earn respect [sources: Saretsky; Vogt].

9. Always Bet on Black?

The titular characters of Tom Stoppard's film " Rosencrantz and Guildenstern Are Dead" begin the film baffled and finally frightened as each of 157 consecutive flips of a coin comes up heads. Guildenstern's explanations of this phenomenon range from time loops to "a spectacular vindication of the principle that each individual coin, spun individually, is as likely to come down heads as tails ... "

Evolution wired humans to see patterns, and our ability to properly process that urge seems to short-circuit the longer we spend gambling. We can rationally accept that independent events like coin flips keep the same odds no matter how many times you perform them.

But we also view those events, less rationally, as streaks, making false mental correlations between randomized events. Viewing the past as prelude, we keep thinking the next flip ought to be tails.

Statisticians call this the gambler's fallacy, aka the Monte Carlo fallacy, after a particularly illustrative example that occurred in that famed Monaco resort town.

During the summer of 1913, bettors watched in increasing amazement as a casino's roulette wheel landed on black 26 times in a row. Inflamed by the certainty that red was "due," the punters kept plunking down their chips. The casino made a mint [sources: Lehrer; Oppenheimer and Monin; Vogt].

8. The Hot Hand and the Monkey's Paw

No discussion of streaks, magical thinking or false causation would be complete without a flip through the sports pages. Stellar sports seasons arise from such a mysterious interplay of factors - natural ability, training, confidence, the occasional X factor - that we imagine patterns in performance, even though studies repeatedly reject streak shooting and "successful" superstitions as anything more than imaginary.

The belief in streaks or slumps implies that success "causes" success and failure "causes" failure or, perhaps more reasonably, that variation in some common factor, such as confidence, causes both. But study after study fails to bear this out [source: Gilovich, et al].

The same holds true for superstitions, although that never stopped retired NBA player and Dallas Mavericks guard Jason Terry from sleeping in the opposing teams' game shorts before each game, or NHL center and retired Ottawa Senators player Bruce Gardiner from dunking his hockey stick in the toilet to break the occasional slump [source: Exact Sports].

The sophomore slump, too, typically arises from a too-good first year. Performance swings tend to even out in the long run, a phenomenon statisticians call regression toward the mean [source: Barnett, et al]. In sports, this averaging out is aided by the opposition, which adjusts to counter the new player's successful skill set.

7. Hormonal Imbalance

Randomized controlled trials are the gold standard in statistics, but sometimes - in epidemiology, for example - ethical and practical considerations force researchers to analyze available cases.

Unfortunately, such observational studies risk bias, hidden variables and, worst of all, study groups that might not accurately reflect the population. Studying a representative sample is vital; it allows researchers to apply results to people outside of the study, like the rest of us.

A case in point: hormone replacement therapy (HRT) for women. Beyond treating symptoms associated with menopause, it was once hailed for potentially reducing coronary heart disease (CHD) risk, thanks to a much-ballyhooed 1991 observational study [source: Stampfer and Colditz].

But later randomized controlled studies, including the large-scale Women's Health Initiative, revealed either a negative relationship, or a statistically insignificant one, between HRT and CHD [source: Lawlor, et al.].

Why the difference? For one thing, women who use HRT tend to come from higher socioeconomic strata and receive better quality of diet and exercise - a hidden explanatory relationship for which the observational study failed to fully account [source: Lawlor, et al].

6. Super Bowl Stock Market Shuffle

In 1978, sports reporter and columnist Leonard Koppett mocked the causation-correlation confusion by wryly suggesting that Super Bowl outcomes could predict the stock market. It backfired: Not only did people believe him, but it worked - with frightful frequency.

The proposal, now commonly known as the Super Bowl Indicator, went as follows: If one of the 16 original National Football League teams - those in existence before the NFL's 1966 merger with the American Football League - won the Super Bowl, the stock market would rise throughout the rest of the year. If a former AFL team won, it would go down [source: Bonsal].

From 1967 to 1978, Koppett's system went 12 for 12; up through 1997, it boasted a 95 percent success rate. It stumbled during the dot-com era (1998-2001) and notably in 2008, when the Great Recession hit, despite a win by the New York Giants (NFC). Still, as of 2022, the indicator had a 73 percent success rate [source: Chen].

Some have argued that the pattern exists, driven by belief; it works, they say, because investors believe it does, or because they believe that other investors believe it.

This notion, though clever in a regressive sort of way, hardly explains the 12 years of successful correlations predating Koppett's article. Others argue that a more relevant pattern lies in the stock market's large-scale upward trend, barring some short-term major and minor fluctuations [source: Johnson].

5. Big Data, Little Clarity

Big data - the process of looking for patterns in data sets so large they resist traditional methods of analysis - rates big buzz in the boardroom [source: Arthur]. But is bigger always better?

It's a rule that's drummed into most researchers in their first stats class: When encountering a sea of data, resist the urge to go on a fishing expedition. Given enough data, patience and methodological leeway, correlations are almost inevitable, if unethical and largely useless.

After all, the mere correlation between two variables does not imply causation; nor does it, in many cases, point to much of a relationship.

For one thing, researchers cannot use statistical measures of correlation willy-nilly; each contains certain assumptions and limitations that fishing expeditions too often ignore, to say nothing of the hidden variables, sampling problems and flaws in interpretation that can gum up a poorly designed study.

But big data is increasingly being used and hailed for its invaluable contributions to areas such as creating customized learning programs; wearable devices that provide real-time feed to your electronic health records; and music streaming services that give you targeted recommendations [source: IntelliPaat]. Just don't expect too much out of big data in the causality department.

4. Minimum Wage Equals Maximum Unemployment

Any issue dealing with money is bound to be deeply divisive and highly politicized, and minimum wage increases are no exception. The arguments are varied and complex, but essentially one side contends that a higher minimum wage hurts businesses, which drives down job availability, which hurts the poor.

The other side responds that there's little evidence for this claim, and that the 76 million Americans working at or below minimum wage, which some argue is not a living wage, would benefit from such an increase. They argue that the federal minimum wage for covered, nonexempt employees ($7.25 per hour in September 2023) has lowered Americans' purchasing power by more than 20 percent [sources: U.S. Department of Labor; Cooper, et al].

As literary critic George Shaw reportedly quipped, "If all the economists were laid end to end, they'd never reach a conclusion," and the minimum-wage debate seems to bear that out [source: Quote Investigator]. For every analyst who says minimum wage increases drive jobs away, there is another who argues against such a correlation.

In the end, both sides share a fundamental problem: namely, the abundance of anecdotal evidence many of their talking heads rely on for support. Secondhand stories and cherry-picked data make for weak tea in any party, even when presented in pretty bar charts.

3. Breakfast Beats Obesity, Dinner Denies Drugs

Between fitness apps, drugs and surgeries, weight loss in the United States is a $78 billion-per-year industry, with millions of Americans bellying up to the weight-loss bar annually [source: Research and Markets]. Not surprisingly, weight loss studies - good, bad or ugly - get a lot of press in the U.S.

Take the popular idea that eating breakfast beats obesity, a sugar-frosted nugget derived from two main studies: One, a 1992 Vanderbilt University randomized controlled study, showed that reversing normal breakfast habits, whether by eating or not eating, correlated with weight loss; the other, a 2002 observational study by the National Weight Control Registry, correlated breakfast-eating with successful weight-losers - which is not the same as correlating it with weight loss [sources: Brown, et al.; Schlundt, et al.; Wyatt, et al.].

Unfortunately, the NWCR study failed to control for other factors - or, indeed, establish any causal connection from its correlation. For example, a person who wants to lose weight might work out more, eat breakfast or go whole-hog protein, but without an experimental design capable of dialing in causal links, such behaviors amount to nothing more than commonly co-occurring characteristics [source: Brown, et al].

A similar problem plagues the numerous studies linking family dinners with a decreased risk of drug addiction for teens. Although attractive for their simple, appealing strategy, these studies frequently fail to control for related factors, such as strong family connections or deep parental involvement in a child's life [source: Miller, et al].

2. The Suicidal Sex

We often hear that men, especially young men, are more likely to commit suicide than are women. In truth, such statements partake of empirical generalization - the act of making a broad statement about a common pattern without attempting to explain it - and mask several known and potential confounding factors.

Take, for example, a Youth Risk Behaviors Survey from 2021 found that girls in grades 9-12 attempted suicide almost twice as often as male students (13 percent vs. 7 percent) [source: American Foundation for Suicide Prevention].

How, then, can a higher correlation exist between the opposite sex and suicide? The answer lies in suicide attempts by methodology: While the most common method of suicide for both sexes in 2020 was by firearm (57.9 percent for men and 33.0 percent for women), women were almost equally likely to die by poisoning or suffocation [source: National Institute of Mental Health].

Even if we could dispose of such confounding factors, the fact would remain that maleness, per se, is not a cause. To explain the trend, we need to instead identify factors common to men, or at least suicidal ones.

The same point applies to the comparatively high rates of suicide reported among divorced men. Divorce doesn't cause men to commit suicide; if anything, it's more indicative of an underlying causal relationship with factors such as male role inflexibility, their social networks, the increasing importance of child care and men's desire for control in relationships [source: Scourfield and Evans].

1. Vaccination Vexation

No correlation/causation list would be complete without discussing parental concerns over vaccination safety. Before the COVID-19 pandemic hit the world in 2020, the main issue was a fear among some parents that the measles, mumps and rubella vaccination was causally linked to autism spectrum disorders. This notion was popularized by celebrities like Jenny McCarthy.

Despite the medical community debunking the 1998 Andrew Wakefield paper that inspired the falsehood, and despite subsequent studies showing no causal link, some parents remain fearful of an autism connection or other vaccine-related dangers [sources: Park; Sifferlin; Szabo].

Then COVID-19 arrived, and to date has killed millions around the globe. Scientists raced to create an effective vaccine and they succeeded; the first U.S. COVID-19 vaccine was available in December 2020 under the FDA's emergency use authorization [source: FDA]. But it also quickly became intertwined with the extreme polarization of U.S. politics and misinformation.

Many parents, especially Republicans, feared the vaccines were unsafe because they were developed so quickly, and because there might be as-yet-unknown long-term side effects. There were also incorrect fears about the vaccine affecting future fertility. Those have now been proven false [source: Kelen and Maragakis].

As of January 2022, just 28 percent of 5- to 11-year-olds had received at least one dose of the vaccine, disappointing many in the medical field [sources: Hamel, Kates]. The number of vaccinated children is growing; by May 2023, 40 percent of 5- to 11-year-olds had received at least done dose [source: CDC].

These are no harmless misunderstandings. Despite debunking a link between autism and childhood vaccines, many parents remain leery of the shots. In 2019, there were 1,282 cases of measles in 31 states, the highest number in the U.S. since 1992. The majority of these cases were among the unvaccinated [source: CDC].

Whether that correspondence is coincidental, correlative or causal is well worth considering. And the effects of the current COVID-19 vaccination hesitation remain to be seen.

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Causation vs. Correlation Explained With 10 Examples

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If you step on a crack, you'll break your mother's back. Surely you know this jingle from childhood. It's a silly example of a correlation with no causation. But there are some real-world instances that we often hear, or maybe even tell?

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Definition: Sometimes if a particular product is said to be in the category of 100%, it does not necessarily mean that it is true. This is an example of All Natural.
Connection: The link provided contains some details regarding the deadly romaine lettuce which is a 100% natural vegetable.

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The E. coli outbreak linked to romaine lettuce has claimed its first victim, the Centers for Disease Control and Prevention said this week in an updated warning.

Not much is known about the person who died, other than that they lived in California. In recent days, the CDC has intensified its advisories about consuming romaine lettuce due to an outbreak of E. coli that has showed up in half of the United States.

The illnesses were initially traced to bagged, chopped lettuce from Yuma, Arizona. But now the agency says "no common grower, supplier, distributor, or brand has been identified" as the culprit. As a result, the CDC is telling people to stay away from romaine lettuce completely for now.

The agency said Wednesday that 23 more people from 10 states have gotten sick since the agency's last update on April 27, 2018.

At least 121 people in 25 states have been sicken over the past few months, the CDC said, with 52 of them requiring hospitalization. New states that have reported E. coli cases include Kentucky, Massachusetts and Utah.

"Based on new information, CDC is expanding its warning to consumers to cover all types of romaine lettuce from the Yuma, Arizona growing region," the agency said last week."This warning now includes whole heads and hearts of romaine lettuce, in addition to chopped romaine and salad mixes containing romaine."

Of the 121 people sickened, 52 of them have been hospitalized, including "14 people who have developed a type of kidney failure called hemolytic uremic syndrome," the CDC said.

Now you may be thinking that premade salads and salad mixes may be all right, but the agency says if it contains any variety of romaine lettuce, it may be best not to eat it unless you know exactly where it's from.

If you want to continue to buy romaine lettuce from a local retailer or produce dealer, here are four must-dos:

Before you go to your local grocery store and pick up some romaine lettuce or eat it at a restaurant, you should confirm with the store or eatery that it is not chopped romaine lettuce from the Yuma, Arizona growing region.

If you've already bought chopped romaine lettuce from anywhere in the United States, the CDC says you "should not eat it and should throw it away, even if some of it was eaten and no one has gotten sick."

The CDC is also asking any restaurants not to sell romaine lettuce from the Yuma region.

Don't know if it's romaine? Throw it away. Better to be safe than sorry.

If you're determined to make your own salad, there are some things you should always keep in mind when handling lettuce, courtesy of the Canada Public Health Agency.

Wash your hands: For the best practice of hygiene, thoroughly wash your hands before and after handling lettuce.

Clean your lettuce: Spray it in your kitchen sink. Letting it soak won't necessarily wash foreign material off the leaves.

Tidy up your eating area: Foodborne disease spreads when uncleaniness happens, so wash and wipe down where you eat.

RELATED: 2 million eggs recalled due to salmonella risk

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Update: Death linked to romaine lettuce as illness spreads to 25 state | Atlanta: News, Weather and Traffic

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The E. coli outbreak linked to romaine lettuce has claimed its first victim, the Centers for Disease Control and Prevention said this week in an updated warning. Not much is known about the person who died, other than that they lived in California.

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Post #7 Reason for Belief: Patternicity (video)

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Post #7 Reason for Belief: Patternicity (video)

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Definition: Patternicity is the natural ability of people to identify a pattern, shape or symbol in a photo or a view they see even if that object does not actually exist.
Connection: The attached video shows patternicity by comparing people with the objects that they are reminded of or see it.

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Post #8 Reason for Belief: Unexplained (image)

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Post #8 Reason for Belief: Unexplained (image)

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Definition: Unexplained can be seen as things which have no proof or explanation of it's existence and sometimes is beyond our knowledge to understand.
Connection: The image attached below shows the appearance of a person in it which still remains unexplained till date.

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Post #9 Reason for Belief: Emotional Connection (image)

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Post #9 Reason for Belief: Emotional Connection (image)

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Definition: Emotional Connection is sometimes developed with someone or it can be said that someone starts to visualize the emotion with that person. When these emotional connections reach to a certain level, a person starts to lose the ability of critical thinking and starts to believe impossible things.
Connection: The image attached shows that the person is greeted by God on his way to heaven.

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Post #10 Critical Thinking: Patternicity

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Post #10 Critical Thinking: Patternicity

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I have chosen the image of a spot animal for critical thinking.
Define Problem: Are the patterns real or is it a kind of illusion that mind creates after seeing the picture for a long time and starting to believe that those patterns are real.
Gather Information:
https://www.scientificamerican.com/article/patternicity-finding-meaningful-patterns/
The website linked above states that our mind develops interpreting stimuli according to the expected model. A book mentioned in the website says that it is our brain that connects the dots and creates a meaning out of the pattern that we see in the nature.
Evaluate Evidence: The website linked contains a theory proposed by the author which has been checked by Harvard University biologist Kevin R. Foster and University of Helsinki biologist Hanna Kokko. He also developed a formula for it: pb>c, where p is the possibility, b is benefit and c is the cost held for belief.
Recognize Assumptions, Emotions and Biases: Michael Shermer, the author assumes that people are very poor at estimating possibility. People are unable to assign casual possibilities to all sets of events that occur around the nature. People usually see many incorrect casual associations of patterns.
Synthesize Evidence, Identify Alternatives: One possible solution is that the appearance of patterns is merely an illusion. Another possible solution is that the patterns are created by complex response to a stimuli created by mind by seeing the image repeatedly.
Select Best Alternative: According to me, the patterns are created by complex response to the stimuli created by the mind after looking at it repeatedly.