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He talks about standard deviation on pg 25. Standard deviation is a different type of statistic. It's like a meta statistic. Its the stat of the stat on how wide of a range the data is in.

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This chapter delves into different types of ways statistics can be portrayed to lead to different conclusions. He highlights medians and means as ways to find the middle of data. It's interesting how data can be manipulated and shown in different ways. It's kinda scary the difference in data portrayal can do.

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One interesting segment of this chapter to me was that from the middle of page 30 to the middle of page 31. It was interesting to me to see the simultaneous good and bad of indexes and how easily a statistic like that can be subtly shifted to favor one datapoint over another.

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This chapter goes over some methods of interpreting data. It starts out giving methods of finding the "middle" of a data set, by mean or median, and why you'd choose either method. It also explains the standard deviation which measures how spread out a dataset is, and the normal distribution which describes a lot of different datasets' distributions. The final important methods of data analysis Chapter 2 goes over are percentiles and indexes, percentiles being groupings of a set of data into evenly sized groups (quartiles, deciles, etc.), and indexes being combinations of multiple statistics into one all-encompassing datapoint that is meant to summarize a dataset (the HDI, sports cars' rankings, etc.).

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The major takeaway from this chapter for me was how statisticians can interpret data using different things. For example, one data set will give us a mean, median, mode, standard deviation, etc. Each value tells us something different, and each is important for a different reason. Also, the author explains how important standard deviation is when evaluating data sets since overlap between two sets is more important then any one value among them.

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One passage that stuck out to me was on page 19. The author explains that "the sensitivity of the mean to outliers is why we should not gauge the economic health of the American middle class by looking at per capita income. Because there has been an explosive growth of incomes at the top end of the distribution...the average income in the United States could be heavily skewed by the megarich." I thought this passage was important because it shows how we must have a good understanding of statistics in order to properly use them. For example, we must know how certain statistically functions, such as mean,  are derived so that we can use them to establish true and accurate claims.

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One passage that I found especially striking was on page 29 when Wheelan describes the tax increase in Illinois. Wheelan notes that "percentage change must not be confused with a change in percentage points" that "the democrats, who engineered this tax increase, pointed out that the state income tax rate was increased by 2 percentage points. The Republicans pointed out that the state income tax had been raised by 67 percent." I thought this was a great example of why understanding statistics is so important to everyday life. This is a very clear example of how easy it is to manipulate statistics in one's favor.

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My major takeaway from chapter 2 was the importance of relativity in descriptive statistics. Wheelan did a great job emphasizing how insignificant statistics are, even if they are accurate, if they don't have any context. I found the example of the HCb2 blood count especially entertaining because I thought it perfectly illustrated the importance of context and perhaps reflected the danger of taking a statistic out of context, that Wheelan referenced many times in his introduction.

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In chapter 2, the passage that I found most interesting was on page 17 which states "From baseball to income, the most basic task when working with data is to summarize a great deal of information." This quote is significant to me because it includes sports and the economy, mixed with math as well. The research is the most fun and most basic process of the stat process. More info means more clean and precise data.

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My major takeaway from Chapter 2 was that their was various ways to understand data and summarize the data through stats. An interesting takeaway would be that the relative benefit of each method (mean median, standard deviation) depends on the data set itself. Wheelan also shared some figures that are understood without surrrounding info. But, other figures need context to understand the graph and data. In conclusion, descriptive stats help provide info for various different contexts.

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In the second Chapter, we learned that, like Wheelan talked about in the chapter before, that there are lots of different ways to collect data, and that there are medians and means, and that learning about the details of how much deviation is regular for a set of data, how much deviation is healthy for an individual getting blood test results back is critical to forming a better understanding of the data

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For me, what most resonated with me was 1) the bit about the bar and Bill Gates on page 19, and 2) the bit about the popcorn on page 24. They made a concept that was a little confusing easier to grasp. Wheelan highlights the fact that raw data needs to be taken with a grain of salt and needs to be analyzed in order to get a set or a result from a set that is applicable to one's situation. If you make sure to look generally at the people in the bar, or generally at the time at which the popcorn kernels are popping, you are much more likely to have your data goofed up by outliers.

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On page 22, this passage resonated with me: "An 'absolute' score, number, or figure has some intrinsic meeting... Absolute figures can usually be interpreted without any context or additional information... A 'relative value or figure has meaning only in comparison to something else, or in some broader context." I thought this was really interesting because I haven't really thought about how certain numbers are automatically widely understood, while others require context. I thought the idea of how some numbers need a wider context or relation to be understood was very interesting, and relevant to math in general, because most numbers require relation to other values or equations to be understood. More practically, I thought it was striking how certain figures that are common within our society seem to have intrinsic meaning, and we can often use statistics to better understand the meaning of those that don't.

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My major takeaway from this chapter was that there are many different ways of trying to understand data and summarize it through descriptive statistics. I learned that the relative benefit of each method, including mean, median, standard deviation,  and indices, depends on the data set itself. The book described each of these concepts and examples of their practical uses, and I learned that their degree of helpfulness depends on the context of the data. Wheelan also talked a lot about how there are some figures that are understood without context (such as temperature), while there are others that need to be in a broader context or related to something else to be understood (such as income). Overall, I got the impression that descriptive statistics are helpful in providing insights into certain phenomena, and different tools are useful in different contexts.

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In this chapter, Wheelan showed us the different ways statisticians use data to infer patterns, by looking at two very different cases of middle class income and the best baseball player. He explained how the different concepts of mean, median, and standard deviation from the mean, all vary and can be both useful and misleading depending on the circumstance. For instance, looking at two different populations that have the same mean will not tell you much if one is skewed due to outliers, in which case median is better. Or, Standard deviation is also important if one population is more spread out. Finally, he also used this chapter to look at relative versus absolute values. Absolute values are more useful for standard things like golf scores, but relative values are better for unknown metrics where you need to compare yourself to others.

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I liked the passage in Chapter 2 that says, "From baseball to income, the most basic task when working with data is to summarize a great deal of information" (17). I think this introduces a lot of the key concepts of statistics in general and also builds off of the question, what is the point, from the first chapter. Ultimately, humans in general can better understand patterns with small amounts of data, which we can do by simplifying large data sets.

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Overall, I took from this chapter that descriptive statistics are incredibly helpful if utilized correctly. They can be great to "definitively" answer questions (like the baseball example), or represent important issues (like the economic health one), but there are many different aspects to consider before drawing a conclusion. Additionally, the framing of the data can totally change the meaning. Depending on the context, either the percentage or raw data can be more or less helpful.

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On page 33, Wheelan writes that "descriptive statistics help to frame the issue. What we do about it, if anything, is an ideological and political question." I think this quote drives home his point that statistics are just one tool to understand an issue. Finding a way to compile or simplify something into a single number to better understand helps people recognize a problem; it's up to people to then do something with the information.

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The major takeaway from his chapter is the characterization of data. Wheelan talks about the Lemon problem in regards to the printers, rather then getting a recall on all of the printers you should looks at single section of them that has the major defect. In addition, looking at the weight of a baseball player or a super-car. The weight of a number plays a factor into the overall score of that thing.

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On page 19 Wheelan states,"If I were to describe the patrons of this bar as having an average annual income of $91 million, the statement would be both statistically correct and grossly misleading" resonated wit me due to its relevancy within today's presidential race. The general conservative conception of the economy in 2020 is that Trump's administration has grown the economy and provided more wealth for the average american. In reality the 5% has had the real growth. Even though the middle class American's income has risen by 4.6% since the post rescission in 2009, the poverty gap has risen by 7.6%. It's more important to characterize our country as a whole rather then a single group.

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The overall impression that I got from this chapter was that descriptive statistics provide a simple, easy to understand summary of a large set of data. However, they can be quite limiting and deceiving depending on how they are determined. Additionally, context is extremely important when using descriptive statistics, especially with relative statistics.

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On page 31 the passage "Any index is highly sensitive to the descriptive statistics that are cobbled together to build it, and to the weight given to each of those components" resonated with me. I thought this was a nice conclusion to the "Car and Drivers" example on page 30. This was interesting to me because it showed the variation in indexes depending on small adjustments of weight given to each category considered.

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Here is what I took away from this chapter...

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On Page # XX the passage "quote passage here" resonated with me...

.... yadda yadda yadda... (write about what you found striking or wonder about said passage).