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The Cartoon Guides
The Cartoon Guide to Statistics
If you have ever looked for P-values by shopping at P mart, tried to watch the Bernoulli Trails on "People's Court," or think that the standard deviation is a criminal offense in six states, then you need The Cartoon Guide to Statistics to put you on the road to statistical literacy.
The Cartoon Guide to Statistics covers all the central ideas of modern statistics: the summary and display of data, probability in gambling and medicine, random variables, Bernoulli Trails, the Central Limit Theorem, hypothesis testing, confidence interval estimation, and much more--all explained in simple, clear, and yes, funny illustrations. Never again will you order the Poisson Distribution in a French restaurant!
230 pages, Paperback
Published January 1, 1993
318 people are currently reading
2465 people want to read
About the author
Larry Gonick
45 books295 followersLarry Gonick (born 1946) is a cartoonist best known for The Cartoon History of the Universe, a history of the world in comic book form, which he has been publishing in installments since 1977. He has also written The Cartoon History of the United States, and he has adapted the format for a series of co-written guidebooks on other subjects, beginning with The Cartoon Guide to Genetics in 1983. The diversity of his interests, and the success with which his books have met, have together earned Gonick the distinction of being "the most well-known and respected of cartoonists who have applied their craft to unravelling the mysteries of science" (Drug Discovery Today, March 2005).
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Displaying 1 - 30 of 129 reviews
November 10, 2014
When a man lives alone, he invents ways to keep busy. One summer I decided I was going to become an AP statistics professor. Yes, I know. Anyhow, I spent that summer studying like you would not believe. This was one of the books I read.
This was a rather straightforward approach to statistics. While it has cartoons, there is real mathematics here. I swear!
This was a rather straightforward approach to statistics. While it has cartoons, there is real mathematics here. I swear!
February 15, 2019
If you have ever seen a sheet of statistical formulas and are unfamiliar with it, they look like incomprehensible nonsense. The notation system in statistics is pretty weird, and it lacks the more logical consistence that you find in regular algebra.
People who write about statistics have this same illogical twist in their minds. That is why most books on the subject spend whole chapters explaining easy stuff, and then make huge logical leaps in just one line, leaving the reader puzzled as to where that new formula all of a sudden came from?!
So most books I've read didn't teach me anything. The Cartoon guide to statistics was a partial exception since they finally managed to explain soms ideas understandably. And the drawings weren't annoying, sometimes they even helped the text!
On the other hand, this book also made unexplained logical jumps and sometimes changed notational systems from one page to another. I guess it's that twist in the brain.
This is by far the most accessible book I have found on this subject so far. But that still doesn't mean that it is understandable.
People who write about statistics have this same illogical twist in their minds. That is why most books on the subject spend whole chapters explaining easy stuff, and then make huge logical leaps in just one line, leaving the reader puzzled as to where that new formula all of a sudden came from?!
So most books I've read didn't teach me anything. The Cartoon guide to statistics was a partial exception since they finally managed to explain soms ideas understandably. And the drawings weren't annoying, sometimes they even helped the text!
On the other hand, this book also made unexplained logical jumps and sometimes changed notational systems from one page to another. I guess it's that twist in the brain.
This is by far the most accessible book I have found on this subject so far. But that still doesn't mean that it is understandable.
February 20, 2018
The back blurb advertises that this book will “put you on the road to statistical literacy.” But unless you already understand statistics or are very comfortable with algebra and have a basic understanding of calculus, you are unlikely to come out of it any more statistically literate than you went in. It uses a lot of symbols and only barely introduces the underlying math. And it is more of an overview of how to do statistics than a guide to understanding statistics.
The book’s biggest lack is that it doesn’t provide any sense of when statistics are being used in a valid manner.
For example, they talk about the very important problem of false positives in tests for disease, or guilt: that in a large enough population and given a false positive rate, tests are more likely to be wrong than right. They conclude that, in order to be more certain that a patient is in fact sick, “The doctor orders more tests.”
They leave out that the doctor had better order different tests that are unrelated to the one already performed. This is a critical step that is often deliberately ignored in public policy debates.
Similarly, they talk about how you use the median instead of the mean to avoid outliers, but not when it is appropriate to avoid outliers and when it is not.
Critically in today’s world of data mining for conclusions, they mention in passing that using the .05 confidence level still means that one in twenty times the null hypothesis is correct even though the interpretation says otherwise. But they do not mention what this means for statistical analyses that look through lots of data for correlations. That kind of data mining is statistically invalid not just because it turns the scientific method on its head, but because it guarantees a statistically invalid result.
These are what most people need to be “on the road to statistical literacy.” The equivalent in literal literacy would be a book that goes into great detail about how letters and combinations of letters are pronounced; that mentions in passing that sometimes these pronunciations are combined to form words; but not what those words are or what they are used for. Following such a course, a person might very well be able to perfectly pronounce words, sentences, and paragraphs but would have no idea what those things mean. They would be able to construct their own strings of letters, pronounce them perfectly, and still have created nothing but gibberish.
Countering statistical gibberish ought to be the reason for becoming statistically literate.
This book appears to have been designed more as a text book for an actual college course; besides assuming some calculus, it also assumes that you have a software statistics package.
I suspect that even after writing this book with his co-author, Larry Gonick had no better understanding of statistics than he’d had before. Most of his jokes either make fun of the fact that there is math, or make puns on statistical terms without regard to their meaning. This is in contrast to Gonick’s other cartoon guides, especially his Cartoon Histories, in which the jokes are related to the topic and so provide a memory aid for that topic.
The book’s biggest lack is that it doesn’t provide any sense of when statistics are being used in a valid manner.
For example, they talk about the very important problem of false positives in tests for disease, or guilt: that in a large enough population and given a false positive rate, tests are more likely to be wrong than right. They conclude that, in order to be more certain that a patient is in fact sick, “The doctor orders more tests.”
They leave out that the doctor had better order different tests that are unrelated to the one already performed. This is a critical step that is often deliberately ignored in public policy debates.
Similarly, they talk about how you use the median instead of the mean to avoid outliers, but not when it is appropriate to avoid outliers and when it is not.
Critically in today’s world of data mining for conclusions, they mention in passing that using the .05 confidence level still means that one in twenty times the null hypothesis is correct even though the interpretation says otherwise. But they do not mention what this means for statistical analyses that look through lots of data for correlations. That kind of data mining is statistically invalid not just because it turns the scientific method on its head, but because it guarantees a statistically invalid result.
These are what most people need to be “on the road to statistical literacy.” The equivalent in literal literacy would be a book that goes into great detail about how letters and combinations of letters are pronounced; that mentions in passing that sometimes these pronunciations are combined to form words; but not what those words are or what they are used for. Following such a course, a person might very well be able to perfectly pronounce words, sentences, and paragraphs but would have no idea what those things mean. They would be able to construct their own strings of letters, pronounce them perfectly, and still have created nothing but gibberish.
Countering statistical gibberish ought to be the reason for becoming statistically literate.
This book appears to have been designed more as a text book for an actual college course; besides assuming some calculus, it also assumes that you have a software statistics package.
I suspect that even after writing this book with his co-author, Larry Gonick had no better understanding of statistics than he’d had before. Most of his jokes either make fun of the fact that there is math, or make puns on statistical terms without regard to their meaning. This is in contrast to Gonick’s other cartoon guides, especially his Cartoon Histories, in which the jokes are related to the topic and so provide a memory aid for that topic.
April 13, 2013
"If you can't explain it simply you don't understand it well enough" and Larry understands his topic matters more than most tenured professors I know.
January 27, 2021
i think statistics is horrible when you learn it in a serious stat 101 theoretical class but once you make it through that pain and understand what the central limit theorem really means and how confidence intervals and hypothesis testing kind of works then this book is fun and makes lots of intuitive sense. this book alone probably won't teach statistics to a person trying to learn it but it is something fun to read for more intuition about what is going on. also nice pictures and graphs which is the most important thing about data
November 9, 2021
Chapter 1: What is statistics?
5/10
Intro chapter. A few comics and then the chapters presented.
Chapter 2: Data description
6/10
I already see how much of this is outdated in the presentation. It’s still readable, but it’s clunky and often I got stuck on words or outdated explanations. Which is kinda an issue as the stats alone are already very complicated for most readers. Why complicate it even more?
The lack of colors is a huge issue actually. The book is way less interesting this way. No character feels alive. Even the lines are incomplete and thick. It’s not really up to par with proper comics, but it’s not bad either. Just painfully mediocre and boring. The cartoon text is silly, but then nothing is funny or daring. Joke characters are just saying silly lines. They really could have pushed it a bit further and added some conflict or spicy jokes at times.
I had to read the stem-and-left page 3-4 times before I got it, again illustrating how far away my mind is from the book. The issue is simple. In the book they present numbers like this: “12 : 0155005”. I just couldn’t figure out what it meant. Then suddenly it hit me. In Excel individual numbers are separated, at minimum, by commas, but I assume back then Excel was not something everyone used. It’s actually: 0, 1, 5, 5, 0, 0, 5. Individual numbers not one single number. My brain just couldn’t see it for some reason and it would have been such an easy job to update the book and add commas and Excel examples.
They also don’t explain things like why you have to take the square root to get the standard deviation. It’s nice they explain what it is, but the math is not explained and just confuses me.
Overall the chapter is fine, but colors and updated terms would make it more interesting.
Chapter 3: Probability
6,5/10
Better in some ways, worse in other ways. It’s a step up in complications and frankly I didn’t really study the formulas because I will likely never have a job where I will use this.
The cartoon characters are better as now we also have 2 historical figures which adds the depth I was asking for. There are also some good explanations using dice to illustrate probabilities. This is a fine enough intro. I actually think this chapter is great for people wanting the basics on probability stats.
Chapter 4: Random variables
5/10
Very math heavy. As I didn’t plan to study the formulas or even understand them unless they were plainly explained this was not really for me. It’s a ton of complicated formulas and my main issue is that they just use math letters so it all looks like a foreing language unless you memorize what every single letter stands for, which again is not really what I personally want to do while reading this book for fun.
The math is simple enough so that I would easily understand it if they just used words instead of symbols. It wouldn't be “proper” math, but it would be beginner friendly this way. If I wanted to understand and memorize all the formulas I’d watch Khan Academy math videos instead as that would make it more simple and direct. Then I’d do some interactive math tests to train my memory. But it wouldn't really work anyhow. When I need to learn a formula to use it for some calculations I learn it in a day. When I need it for an exam or something equally “pointless” it’s 10 times harder.
Chapter 5: A tale of two distributions
4/10
More formulas. This is good info for the right people who crucially need this stuff and already understand complicated algebra math on a high level. For me just wanting the basics it’s a miss. That’s not to say it’s bad. I haven’t even checked out the formulas. This may be some amazing math for all I know.
Chapter 6: Sampling
5/10
Again formula focus. Most formulas are not even applied so I see the formulas, but then don’t really see them in action. There are some okay general points about sampling, but this is just badly explained. There is nothing more to it. It’s not just me not needing the math and therefore skipping it. It’s even just the math now not being fully explained. Rather they just show formulas and then hazily explain what they sorta do. This is impossible for me to fully understand unless I seek out other sources too.
Chapter 7: Confidence intervals
6/10
I like some examples in the book. The dice examples before and now the arrow circles examples. They work really well, unfortunately there are very few of them. Such a comic book should be all about such visual overviews and then the hard stats could be secondary info.
We see a lot of methods to calculate confidence intervals. I’m not sure where each formula works best because they say things like: for small samples use this formula for big samples use this other formula. What is a big sample? I must have missed that. Maybe at the end they could have shown all formulas with names and then also used words for symbols too so that you had a clear overview. My memory is terrible and I can’t remember all this math unless I use it every day for a month.
Chapter 8: Hypothesis testing
2,5/10
Goes into culture war territory and makes some crucial logical errors. Not ideal.
I’m not checking the math itself, but it’s quite telling that the more simple logical arguments have been less than ideal. I noticed quite a few progressive political statements in the book before, but just ignored it because it was just single statements. Here it’s a logical fallacy. They start the chapter by claiming that stats are misused by social science and politicians. Then they use a “racism” example where there are only 4 Black people in a group of 80 while they are 50% of the population. It would be like expecting 50% of nurses to be male and then blame sexism against men for the “unfair” distribution. Which actually is the main way social science misuses stats - something they themselves just called out. They are blindly assuming that both groups are exactly the same biologically, and maybe culturally, and that any difference is caused by the exact discrimination effect they are supporting. It’s like saying fire is hot because it lacks water then as you measure the temperature of fire and prove it’s hot you conclude that your prior hypothesis is proven correct.
Chapter 9: Comparing two populations
5/10
With more and more fallacies and ideological biases appearing and the book really not getting any more interesting I feel like it has overstayed its welcome. Here they go into gender wage differences, in the prior chapter it was racial descrimination against African Americans. They also have a lot of examples about other progressive claims. It’s just a tad too much. It’s starting to feel out of place in a stats book in my opinion.
But that’s not really the main issue at all. The book is just boring, looks unappealing and after a while gets too dry. It’s more formulas and more examples that are in writing instead of being drawings. The drawings are too often just jokes. This chapter for example has 2 creepy weird male characters make fun of math terms with sexual innuendos as 2 attractive, unfairly underpaid, women sigh at them. Instead the jokes and examples should have been like the dice and arrow drawings illustrating the stats in a clear way. The issue is that it’s not relevant to statistics and furthermore makes this impossible to recommend for readers who are not progressive ideologically.
Chapter 10: Experimental design
5/10
Clear but boring. This chapter is an intro to the basics of experimental design. But this is an intro you get in any proper academic textbook at your university. And you need way more examples to understand all of this.
Chapter 11: Regression
5/10
Clear but boring. This is advanced regression analysis and even ANOVA outputs are shown so it’s too high a level for such a silly comic book. I did notice that the book became unbiased again in the last 2 chapters which I think is very nice.
Chapter 12: Conclusion
3/10
Extra math concept mentioned.
My final opinion on the book
Boring, biased, dry, old and colorless.
I don’t use stats for a job so it’s not really something I forced myself to memorize. By reading this book I just wanted to understand the basics and see if I could have fun with it. The right book can make even a dry topic fun for laymen. This is just not it. This is for math nerds getting A+ in math starting in a stats class and just wanting a fast refresher book to get going. They will recognize half the formulas, but still struggle with the rest. Unless you remember 20 stats formulas already this is just not for your level. It’s way too advanced.
Then the colorless pages, bad humor and bias didn’t really help. It’s definitely not a book you can blindly recommend to students unless they are left-wing politically.
Overall it’s just boring. That’s the main issue. I didn’t have fun reading any of it. Not even the “jokes” or drawn examples besides max 5 examples out of 100.
This is a book for people who work with stats already. So people who use formulas to work with big data and everything is explained from that point of view. I do want to understand stats. I just don’t need a detailed guide about producing them. What I really want is a book using academic papers as examples. This is where I see 95% of the advanced stats I want to understand better. So psychology papers with various tables and results. If a book just went over 10 such papers and explained all tables that would be perfect for me. I can’t find a single post, website, video or example of anyone doing that: going over how all results were calculated in academic papers. Of course this is the “loser” side of the equation.
I can’t really comment much on the math in the book as I didn’t go over it. If it has as many mistakes as the logical points, that's not good at all. I have read newer and more colorful stats books and recommend them instead. I used to work a bit with stats and read academic books to learn it. It was fun and many modern stats books are colorful and funny. But right now that’s not what I need.
5/10
Intro chapter. A few comics and then the chapters presented.
Chapter 2: Data description
6/10
I already see how much of this is outdated in the presentation. It’s still readable, but it’s clunky and often I got stuck on words or outdated explanations. Which is kinda an issue as the stats alone are already very complicated for most readers. Why complicate it even more?
The lack of colors is a huge issue actually. The book is way less interesting this way. No character feels alive. Even the lines are incomplete and thick. It’s not really up to par with proper comics, but it’s not bad either. Just painfully mediocre and boring. The cartoon text is silly, but then nothing is funny or daring. Joke characters are just saying silly lines. They really could have pushed it a bit further and added some conflict or spicy jokes at times.
I had to read the stem-and-left page 3-4 times before I got it, again illustrating how far away my mind is from the book. The issue is simple. In the book they present numbers like this: “12 : 0155005”. I just couldn’t figure out what it meant. Then suddenly it hit me. In Excel individual numbers are separated, at minimum, by commas, but I assume back then Excel was not something everyone used. It’s actually: 0, 1, 5, 5, 0, 0, 5. Individual numbers not one single number. My brain just couldn’t see it for some reason and it would have been such an easy job to update the book and add commas and Excel examples.
They also don’t explain things like why you have to take the square root to get the standard deviation. It’s nice they explain what it is, but the math is not explained and just confuses me.
Overall the chapter is fine, but colors and updated terms would make it more interesting.
Chapter 3: Probability
6,5/10
Better in some ways, worse in other ways. It’s a step up in complications and frankly I didn’t really study the formulas because I will likely never have a job where I will use this.
The cartoon characters are better as now we also have 2 historical figures which adds the depth I was asking for. There are also some good explanations using dice to illustrate probabilities. This is a fine enough intro. I actually think this chapter is great for people wanting the basics on probability stats.
Chapter 4: Random variables
5/10
Very math heavy. As I didn’t plan to study the formulas or even understand them unless they were plainly explained this was not really for me. It’s a ton of complicated formulas and my main issue is that they just use math letters so it all looks like a foreing language unless you memorize what every single letter stands for, which again is not really what I personally want to do while reading this book for fun.
The math is simple enough so that I would easily understand it if they just used words instead of symbols. It wouldn't be “proper” math, but it would be beginner friendly this way. If I wanted to understand and memorize all the formulas I’d watch Khan Academy math videos instead as that would make it more simple and direct. Then I’d do some interactive math tests to train my memory. But it wouldn't really work anyhow. When I need to learn a formula to use it for some calculations I learn it in a day. When I need it for an exam or something equally “pointless” it’s 10 times harder.
Chapter 5: A tale of two distributions
4/10
More formulas. This is good info for the right people who crucially need this stuff and already understand complicated algebra math on a high level. For me just wanting the basics it’s a miss. That’s not to say it’s bad. I haven’t even checked out the formulas. This may be some amazing math for all I know.
Chapter 6: Sampling
5/10
Again formula focus. Most formulas are not even applied so I see the formulas, but then don’t really see them in action. There are some okay general points about sampling, but this is just badly explained. There is nothing more to it. It’s not just me not needing the math and therefore skipping it. It’s even just the math now not being fully explained. Rather they just show formulas and then hazily explain what they sorta do. This is impossible for me to fully understand unless I seek out other sources too.
Chapter 7: Confidence intervals
6/10
I like some examples in the book. The dice examples before and now the arrow circles examples. They work really well, unfortunately there are very few of them. Such a comic book should be all about such visual overviews and then the hard stats could be secondary info.
We see a lot of methods to calculate confidence intervals. I’m not sure where each formula works best because they say things like: for small samples use this formula for big samples use this other formula. What is a big sample? I must have missed that. Maybe at the end they could have shown all formulas with names and then also used words for symbols too so that you had a clear overview. My memory is terrible and I can’t remember all this math unless I use it every day for a month.
Chapter 8: Hypothesis testing
2,5/10
Goes into culture war territory and makes some crucial logical errors. Not ideal.
I’m not checking the math itself, but it’s quite telling that the more simple logical arguments have been less than ideal. I noticed quite a few progressive political statements in the book before, but just ignored it because it was just single statements. Here it’s a logical fallacy. They start the chapter by claiming that stats are misused by social science and politicians. Then they use a “racism” example where there are only 4 Black people in a group of 80 while they are 50% of the population. It would be like expecting 50% of nurses to be male and then blame sexism against men for the “unfair” distribution. Which actually is the main way social science misuses stats - something they themselves just called out. They are blindly assuming that both groups are exactly the same biologically, and maybe culturally, and that any difference is caused by the exact discrimination effect they are supporting. It’s like saying fire is hot because it lacks water then as you measure the temperature of fire and prove it’s hot you conclude that your prior hypothesis is proven correct.
Chapter 9: Comparing two populations
5/10
With more and more fallacies and ideological biases appearing and the book really not getting any more interesting I feel like it has overstayed its welcome. Here they go into gender wage differences, in the prior chapter it was racial descrimination against African Americans. They also have a lot of examples about other progressive claims. It’s just a tad too much. It’s starting to feel out of place in a stats book in my opinion.
But that’s not really the main issue at all. The book is just boring, looks unappealing and after a while gets too dry. It’s more formulas and more examples that are in writing instead of being drawings. The drawings are too often just jokes. This chapter for example has 2 creepy weird male characters make fun of math terms with sexual innuendos as 2 attractive, unfairly underpaid, women sigh at them. Instead the jokes and examples should have been like the dice and arrow drawings illustrating the stats in a clear way. The issue is that it’s not relevant to statistics and furthermore makes this impossible to recommend for readers who are not progressive ideologically.
Chapter 10: Experimental design
5/10
Clear but boring. This chapter is an intro to the basics of experimental design. But this is an intro you get in any proper academic textbook at your university. And you need way more examples to understand all of this.
Chapter 11: Regression
5/10
Clear but boring. This is advanced regression analysis and even ANOVA outputs are shown so it’s too high a level for such a silly comic book. I did notice that the book became unbiased again in the last 2 chapters which I think is very nice.
Chapter 12: Conclusion
3/10
Extra math concept mentioned.
My final opinion on the book
Boring, biased, dry, old and colorless.
I don’t use stats for a job so it’s not really something I forced myself to memorize. By reading this book I just wanted to understand the basics and see if I could have fun with it. The right book can make even a dry topic fun for laymen. This is just not it. This is for math nerds getting A+ in math starting in a stats class and just wanting a fast refresher book to get going. They will recognize half the formulas, but still struggle with the rest. Unless you remember 20 stats formulas already this is just not for your level. It’s way too advanced.
Then the colorless pages, bad humor and bias didn’t really help. It’s definitely not a book you can blindly recommend to students unless they are left-wing politically.
Overall it’s just boring. That’s the main issue. I didn’t have fun reading any of it. Not even the “jokes” or drawn examples besides max 5 examples out of 100.
This is a book for people who work with stats already. So people who use formulas to work with big data and everything is explained from that point of view. I do want to understand stats. I just don’t need a detailed guide about producing them. What I really want is a book using academic papers as examples. This is where I see 95% of the advanced stats I want to understand better. So psychology papers with various tables and results. If a book just went over 10 such papers and explained all tables that would be perfect for me. I can’t find a single post, website, video or example of anyone doing that: going over how all results were calculated in academic papers. Of course this is the “loser” side of the equation.
I can’t really comment much on the math in the book as I didn’t go over it. If it has as many mistakes as the logical points, that's not good at all. I have read newer and more colorful stats books and recommend them instead. I used to work a bit with stats and read academic books to learn it. It was fun and many modern stats books are colorful and funny. But right now that’s not what I need.
October 18, 2020
An excellent book! While it may seem like a kid's book, there is no shame in reading casual, "chatty" introductions to a field when you're trying to get the basic intuition behind concepts.
April 5, 2020
2016.05.12–2016.06.26
Contents
Gonick L & Smith W (1993) Cartoon Guide to Statistics
Acknowledgments
01. What Is Statistics?
02. Data Description
• Summary Statistics
• The Mean (or "Average")
• The Median
• Measures of S p r e a d
• Interquartile Range
• Standard Deviation
• Properties of x̅ and s
• An Empirical Rule
03. Probability
• Basic Definitions
• Basic Operations
• Conditional Probability
• Independence and the special multiplication rule
• Bayes' Theorem and the case of the false positives...
04. Random Variables
• Mean and Variance of Random Variables
• Continuous Random Variables
• Mean and Variance of a continuous random variable
• Adding random variables
05. A Tale of Two Distributions
• Bernoulli trial
• The binomial random variable
• The standard normal distribution
• "Fuzzy Central Limit Theorem"
• Continuity correction
06. Sampling
• Sampling Design
• The Simple Random Sample
• Stratified Sampling
• Cluster Sampling
• Systematic Sampling
• Word of warning #1
• Word of warning #2
• Opportunity Sample
• Sample Size & standard error
• Sampling error
• Sampling Distribution of the Mean
• The Central Limit Theorem
• The t-distribution
07. Confidence Intervals
• Estimating Confidence Intervals
• Confidence Intervals for µ
• Student's t (again!)
• Degrees of freedom
08. Hypothesis Testing
• Step 1. Formulate all hypotheses
• Step 2. The Test Statistic
• Step 3. p-value
• Step 4. Compare the p-value to a fixed significance level, α
• Large Sample Significance Test for Proportions
• Large Sample Test for the Population Mean
• Small Sample Test for the Population Mean
• Decision Theory
09. Comparing Two Populations
• Comparing Success Rates (or failure rates) for two populations
• The Model
• Sampling distribution for P^1 - P^2
• Confidence Intervals for p1-p2
• Hypothesis Testing
• The general recipe
• Comparing the Means of two populations
• Confidence intervals
• Hypothesis testing
• and how about comparing Small Sample Means?
• Paired Comparisons: a better way to compare gasolines
10. Experimental Design
• Replication
• Local control
• Randomization
• Latin square
11. Regression
• Regression analysis
• The Regression or Least Squares line
• ANOVA
• ANOVA table
• The squared correlation
• The correlation coefficient
• Statistical Inference
• Confidence intervals
• Hypothesis testing
• Multiple linear regression
• Non-linear regression
• Regression diagnostics
12. Conclusion
• Data Display
• Statistical analysis of Multivariate Data
• • Cluster analysis
• • Discriminant analysis
• • Factor analysis
• Probability
• • Random walks
• • Time series analysis
• • Image analysis
• • Resampling
• Data Quality
• Innovation
• Communication
• Teamwork
Bibliography
Index
Contents
Gonick L & Smith W (1993) Cartoon Guide to Statistics
Acknowledgments
01. What Is Statistics?
02. Data Description
• Summary Statistics
• The Mean (or "Average")
• The Median
• Measures of S p r e a d
• Interquartile Range
• Standard Deviation
• Properties of x̅ and s
• An Empirical Rule
03. Probability
• Basic Definitions
• Basic Operations
• Conditional Probability
• Independence and the special multiplication rule
• Bayes' Theorem and the case of the false positives...
04. Random Variables
• Mean and Variance of Random Variables
• Continuous Random Variables
• Mean and Variance of a continuous random variable
• Adding random variables
05. A Tale of Two Distributions
• Bernoulli trial
• The binomial random variable
• The standard normal distribution
• "Fuzzy Central Limit Theorem"
• Continuity correction
06. Sampling
• Sampling Design
• The Simple Random Sample
• Stratified Sampling
• Cluster Sampling
• Systematic Sampling
• Word of warning #1
• Word of warning #2
• Opportunity Sample
• Sample Size & standard error
• Sampling error
• Sampling Distribution of the Mean
• The Central Limit Theorem
• The t-distribution
07. Confidence Intervals
• Estimating Confidence Intervals
• Confidence Intervals for µ
• Student's t (again!)
• Degrees of freedom
08. Hypothesis Testing
• Step 1. Formulate all hypotheses
• Step 2. The Test Statistic
• Step 3. p-value
• Step 4. Compare the p-value to a fixed significance level, α
• Large Sample Significance Test for Proportions
• Large Sample Test for the Population Mean
• Small Sample Test for the Population Mean
• Decision Theory
09. Comparing Two Populations
• Comparing Success Rates (or failure rates) for two populations
• The Model
• Sampling distribution for P^1 - P^2
• Confidence Intervals for p1-p2
• Hypothesis Testing
• The general recipe
• Comparing the Means of two populations
• Confidence intervals
• Hypothesis testing
• and how about comparing Small Sample Means?
• Paired Comparisons: a better way to compare gasolines
10. Experimental Design
• Replication
• Local control
• Randomization
• Latin square
11. Regression
• Regression analysis
• The Regression or Least Squares line
• ANOVA
• ANOVA table
• The squared correlation
• The correlation coefficient
• Statistical Inference
• Confidence intervals
• Hypothesis testing
• Multiple linear regression
• Non-linear regression
• Regression diagnostics
12. Conclusion
• Data Display
• Statistical analysis of Multivariate Data
• • Cluster analysis
• • Discriminant analysis
• • Factor analysis
• Probability
• • Random walks
• • Time series analysis
• • Image analysis
• • Resampling
• Data Quality
• Innovation
• Communication
• Teamwork
Bibliography
Index
August 25, 2009
I adore Larry Gonick's Cartoon Guide series in general, but that is partly because I clearly identify their purpose. Don't think of this as a College level textbook in statistics because it isn't. It is rather an illustrated, extremely easy to read conceptual overview of statistics, the moral equivalent of Cliff note or a course outline but with cartoons and a certain amount of humor and history mixed in.
Do not underestimate the value of this if you are a student wanting to learn statistics! For many students, the problem with statistics isn't the algebra or computations, which are straightforward if tedious. It is grasping the concepts of statistics -- the notions underlying probability, sampling, distributions, the central limit theorem and Gaussians, how they relate to estimates of error.
This is precisely where most college level statistics texts fall short. They may well present all of the equations needed (and then some). They may well derive them and present examples of their use. However, to my experience they do a fairly mediocre job of simply conveying the idea that underlies all of the algebra and computation. A student is left memorizing dozens of equations and relations without ever gaining a deep understanding of what they mean (or really how to properly use them).
Using the Cartoon Guide to Statistics as a supplement to a college text, however, gives the weaker student a conceptual bridge. Best of all, it is a bridge that they can cross in one sitting! They can read it, cover to cover, in a matter of a few hours, and then refer back to it when an idea confuses them for the rest of the term.
The other group the book is ideal for is younger students in high school (or even middle school), ones whose attention span is not yet up to the task of slogging through a serious book in statistics written in small print by a humourless author. Again such a student would be well-served by having another more mathematical reference handy, but if one's goal is just to convey the idea and methodology underlying the ideas of: distribution, mean of the distribution, variance of the distribution, standard deviation, and the central limit theorem (and what else is there, really?) you can hardly do better.
Do not underestimate the value of this if you are a student wanting to learn statistics! For many students, the problem with statistics isn't the algebra or computations, which are straightforward if tedious. It is grasping the concepts of statistics -- the notions underlying probability, sampling, distributions, the central limit theorem and Gaussians, how they relate to estimates of error.
This is precisely where most college level statistics texts fall short. They may well present all of the equations needed (and then some). They may well derive them and present examples of their use. However, to my experience they do a fairly mediocre job of simply conveying the idea that underlies all of the algebra and computation. A student is left memorizing dozens of equations and relations without ever gaining a deep understanding of what they mean (or really how to properly use them).
Using the Cartoon Guide to Statistics as a supplement to a college text, however, gives the weaker student a conceptual bridge. Best of all, it is a bridge that they can cross in one sitting! They can read it, cover to cover, in a matter of a few hours, and then refer back to it when an idea confuses them for the rest of the term.
The other group the book is ideal for is younger students in high school (or even middle school), ones whose attention span is not yet up to the task of slogging through a serious book in statistics written in small print by a humourless author. Again such a student would be well-served by having another more mathematical reference handy, but if one's goal is just to convey the idea and methodology underlying the ideas of: distribution, mean of the distribution, variance of the distribution, standard deviation, and the central limit theorem (and what else is there, really?) you can hardly do better.
January 15, 2019
My go to probability & statistics refresher
This book has been my go to refresher on probability and statistics for many years now whenever I need to remember something. It is a very simple and fun introduction to many concepts that are important to our every day lives. Yet, it also gets fairly in depth into statistical concepts. The reason I gave he book 4 stars instead of 5 though is that there are places where even the cartoons jump a little too fast for my speed and I need to refer to other resources to fully understand the concept. But, I’ve yet to find a simpler, more accessible statistics book.
This book has been my go to refresher on probability and statistics for many years now whenever I need to remember something. It is a very simple and fun introduction to many concepts that are important to our every day lives. Yet, it also gets fairly in depth into statistical concepts. The reason I gave he book 4 stars instead of 5 though is that there are places where even the cartoons jump a little too fast for my speed and I need to refer to other resources to fully understand the concept. But, I’ve yet to find a simpler, more accessible statistics book.
September 1, 2007
"chicken soup" for people who have to endure courses in statistics, it failed to make me fall in love with the subject, but enough to make we stood in awe with the level of obsessions some people have to measure our lives with numbers. Hooray to all statisticians who provide guidance to understand the world we're living in - but everybody need to remind themselves that we need further look into each phenomenon lest we get disoriented - don't blame the statistics for misunderstanding!
March 10, 2015
There are many math courses that I should have skipped & just read this book instead.
Needs Excel worksheets & macros to help one enjoy/experiment. Might make them myself.
I bought much of the whole Cartoon Guide/History series & leave them near the kids.
Needs Excel worksheets & macros to help one enjoy/experiment. Might make them myself.
I bought much of the whole Cartoon Guide/History series & leave them near the kids.
April 7, 2009
A nice basic review of statistics. I read through this with my stats text from college, looking up derivations/proofs of the important results discussed more intuitively in this book. I think this was a valuable approach, I certainly got more mileage out of this book with this kind of reading.
February 1, 2021
The book's title is 100% self-descriptive; despite the fact that the book is full of comic paintings, it is still a complete and very well explained statistics guide.
The writers use the cartoons to make it more pleasant to read and very good examples to make everything written clear; nevertheless, when it comes to the scientific content they have taken no shortcuts. The book contains all the basic elements of the theory of probability and statistics - I strongly believe that someone that reads and understands this book learns exactly as much as it is taught in university modules. Also, in the last chapter a lot of advanced subjects are mentioned providing guidance on next steps for people that want to learn more on the field.
I am quite familiar with the field so took me only 3 days to finish, but it is a book that in order to be understood requires actual study (i.e. pen and paper in some cases). I totally recommend it and I wish I had discovered it when I was still a student - it is so much more fun to read than my old cartoon-free university books!
The writers use the cartoons to make it more pleasant to read and very good examples to make everything written clear; nevertheless, when it comes to the scientific content they have taken no shortcuts. The book contains all the basic elements of the theory of probability and statistics - I strongly believe that someone that reads and understands this book learns exactly as much as it is taught in university modules. Also, in the last chapter a lot of advanced subjects are mentioned providing guidance on next steps for people that want to learn more on the field.
I am quite familiar with the field so took me only 3 days to finish, but it is a book that in order to be understood requires actual study (i.e. pen and paper in some cases). I totally recommend it and I wish I had discovered it when I was still a student - it is so much more fun to read than my old cartoon-free university books!
January 8, 2020
Simple enough to understand - authors used examples of situations and scenarios as well as illustrations to define concepts, instead of theory and formulae. However, towards the end, more of each page was lined with formulae, which put me off. This led to me skimming through the final few chapters. However, all in all, it's a good guide, and I would recommend it to anyone just starting out with statistics, or anyone who is looking to refresh and cement their learnings before moving on to something more advanced.
February 18, 2020
This was a fun, short read. It took around two hours and was a pretty neat overview of basic stats. Docked a star for some cartoons which were clearly in poor taste.
March 15, 2017
A quick and clear way to get introduced to various terms in statistics. Liked the way cartoon series was presented in an easy way.
March 15, 2014
(4.0) Actually not a bad refresher reference
I don't think anyone would learn statistics from this one, but it actually does pack in a fair amount of justification/explication in addition to some of the basics from the statistician's toolbox. I also have to admit that I never learned how to compute (or the real meaning of) p-values, Student's t-test etc., so was cool to see the motivation/~derivation of the tools there. Definitely fun read and if I needed to refresh my memory of some of the key concepts, I might actually consider starting here.
Might be a good companion to business school or something like that. It would've been inadequate to get me ramped up for grad school though (had to do that the hard way). I know it's not Gonick's style, but including some exercises would've been really cool/helpful. Perhaps he could consider some companion books. Making 'homework' fun with cartoons could be a winning combination.
I don't think anyone would learn statistics from this one, but it actually does pack in a fair amount of justification/explication in addition to some of the basics from the statistician's toolbox. I also have to admit that I never learned how to compute (or the real meaning of) p-values, Student's t-test etc., so was cool to see the motivation/~derivation of the tools there. Definitely fun read and if I needed to refresh my memory of some of the key concepts, I might actually consider starting here.
Might be a good companion to business school or something like that. It would've been inadequate to get me ramped up for grad school though (had to do that the hard way). I know it's not Gonick's style, but including some exercises would've been really cool/helpful. Perhaps he could consider some companion books. Making 'homework' fun with cartoons could be a winning combination.
September 18, 2009
This is the default Christmas present book I'm going to give to parents with middle-school kids. I found it at the MP library's new book section, and it is worth looking at.
It starts out simple - illustrating the differences between median, mean and mode. By page 22, I got a little shaky, but it covers more than what I learned in a business math class in college.
This series also looks at American History, which may also be good.
It starts out simple - illustrating the differences between median, mean and mode. By page 22, I got a little shaky, but it covers more than what I learned in a business math class in college.
This series also looks at American History, which may also be good.
May 14, 2013
Very good concept with impressive cartoon display. Basic concepts of statistics are explained well with a good examples and real life problems. You will not get bored while reading this book unlike other stereotypical statistics books. Authors' sense of humor is great and how they tried to incorporate it into the 'mathematical' cartoons is brilliant. Overall good book for the beginner students who have just started studying statistics.
January 27, 2014
Thought this one was great. It does a great job of gradual learning curve mixed with an emphasis on real world application but it is also unafraid to toss a little math your way. Not to mention it is really funny at times. I've been chewing up stat books lately as an attempt to refresh on these concepts for work. This is a great refresher and then I'd add Data Smart as a good extension to more modern issues (like clustering, and social graph stuff).
December 3, 2011
Oh, CGS. Apparently the only statistics textbook out there without terrible, glaring errors, according to my professor. I really can see the utility of this teaching method, especially for high school - but it doesn't have any practice problems! (what, me, complain about having no homework?) Good as a reference, but it should not be your only source for learning stats.
March 27, 2012
Borrowed from a friend as a quick reference to my statistics homework. While some of the illustrations are helpful, it's far easier to sit down with a tutor for explanations, than to try to understand the illustrations. This is better suited as a gift to someone who already has a rudimentary knowledge of statistics.
September 24, 2013
Funny stuff about statistics! It can be a pretty interesting topic if you are looking at interesting data. It could probably help some folks get over their fear of the topic, I'm not sure, but I do like the cartoons. I pull some of them in to the stats class I'm teaching, just for fun.
March 1, 2012
Fantastic review for my exams. Don't buy this expecting it to teach you statistics, but if you need a review or just want to see the big picture, this is your book.
December 19, 2017
Ne va pas en profondeur dans les technicalités statistiques, tout en étant rigoureux et bien vulgarisé. Un bon complément à un cours de stats.
October 27, 2015
O livro é bom, embora contenha mais fórmulas matemáticas do que eu imaginava.
March 15, 2023
Cuốn sách "The Cartoon Guide to Statistics" là một tài liệu thú vị và dễ hiểu về lĩnh vực thống kê, được viết bởi hai tác giả Larry Gonick và Woollcott Smith. Bằng những hình ảnh dễ nhìn và lời giải thích rõ ràng, tác giả đã giúp độc giả hiểu được những khái niệm và kỹ thuật trong thống kê một cách dễ dàng.
Cuốn sách được chia thành 10 chương, bao gồm các chủ đề như độ đo trung bình, phương sai, độ lệch chuẩn, phân phối chuẩn, kiểm định giả thuyết, và nhiều khái niệm thống kê khác. Tất cả các chương đều được trình bày bằng những hình ảnh hoạt hình dễ nhìn và lời giải thích rõ ràng và đầy đủ.
Điểm đáng chú ý nhất của cuốn sách là cách tác giả giải thích một cách đơn giản những khái niệm thống kê phức tạp. Ví dụ, trong chương 2, tác giả giải thích độ đo trung bình bằng cách sử dụng hình ảnh của một cánh đồng, trong đó chiều cao của cây được đo bằng một thanh đo, và sau đó tính toán độ cao trung bình của tất cả các cây trong cánh đồng đó. Như vậy, tác giả đã giúp người đọc hiểu được ý nghĩa của độ đo trung bình một cách đơn giản và dễ dàng.
Ngoài ra, tác giả cũng đưa ra rất nhiều ví dụ thực tế để minh họa các khái niệm và phương pháp thống kê. Những ví dụ này bao gồm các ứng dụng của thống kê trong kinh tế, y học, sinh học, xã hội học và nhiều lĩnh vực khác.
Bên cạnh đó, cuốn sách cũng giải thích rất chi tiết và dễ hiểu về cách thực hiện các phương pháp thống kê, bao gồm cả việc tính toán và đánh giá kết quả. Điều này rất hữu ích cho những người mới bắt đầu học thống kê.
Một điểm đáng lưu ý khác của cuốn sách là cách tác giả giải thích về các phương pháp và công cụthống kê bằng cách so sánh chúng với các công cụ thông thường mà chúng ta sử dụng trong cuộc sống hàng ngày. Ví dụ như trong chương 6, tác giả giải thích về phương pháp tìm kiếm khoảng tin cậy bằng cách so sánh với cách chúng ta tìm kiếm đồ thất lạc trong nhà. Những so sánh này giúp cho người đọc dễ dàng hình dung và hiểu rõ hơn về các khái niệm và phương pháp thống kê.
Ngoài ra, cuốn sách cũng đưa ra nhiều lời khuyên và kinh nghiệm trong việc áp dụng thống kê vào thực tiễn. Tác giả giải thích rất chi tiết về các bước tiếp cận và phân tích dữ liệu, cũng như các lỗi thường gặp khi áp dụng thống kê và cách khắc phục chúng.
Cuối cùng, đây là một cuốn sách thực sự rất thú vị và dễ hiểu. Tác giả đã thực sự thành công trong việc giải thích các khái niệm và phương pháp thống kê một cách rõ ràng và dễ hiểu, bằng cách sử dụng những hình ảnh hoạt hình vui nhộn và ví dụ thực tế. Cuốn sách cũng cung cấp cho người đọc nhiều kinh nghiệm và lời khuyên về cách áp dụng thống kê vào thực tiễn. Nếu bạn muốn tìm hiểu về thống kê một cách thú vị và dễ hiểu, "The Cartoon Guide to Statistics" là một lựa chọn tuyệt vời cho bạn.
Mình mua sách gốc cuốn này tại Bookee, bạn cần mua thì có thể tham khảo ở đây: https://bookee.store/the-cartoon-guid...
Cuốn sách được chia thành 10 chương, bao gồm các chủ đề như độ đo trung bình, phương sai, độ lệch chuẩn, phân phối chuẩn, kiểm định giả thuyết, và nhiều khái niệm thống kê khác. Tất cả các chương đều được trình bày bằng những hình ảnh hoạt hình dễ nhìn và lời giải thích rõ ràng và đầy đủ.
Điểm đáng chú ý nhất của cuốn sách là cách tác giả giải thích một cách đơn giản những khái niệm thống kê phức tạp. Ví dụ, trong chương 2, tác giả giải thích độ đo trung bình bằng cách sử dụng hình ảnh của một cánh đồng, trong đó chiều cao của cây được đo bằng một thanh đo, và sau đó tính toán độ cao trung bình của tất cả các cây trong cánh đồng đó. Như vậy, tác giả đã giúp người đọc hiểu được ý nghĩa của độ đo trung bình một cách đơn giản và dễ dàng.
Ngoài ra, tác giả cũng đưa ra rất nhiều ví dụ thực tế để minh họa các khái niệm và phương pháp thống kê. Những ví dụ này bao gồm các ứng dụng của thống kê trong kinh tế, y học, sinh học, xã hội học và nhiều lĩnh vực khác.
Bên cạnh đó, cuốn sách cũng giải thích rất chi tiết và dễ hiểu về cách thực hiện các phương pháp thống kê, bao gồm cả việc tính toán và đánh giá kết quả. Điều này rất hữu ích cho những người mới bắt đầu học thống kê.
Một điểm đáng lưu ý khác của cuốn sách là cách tác giả giải thích về các phương pháp và công cụthống kê bằng cách so sánh chúng với các công cụ thông thường mà chúng ta sử dụng trong cuộc sống hàng ngày. Ví dụ như trong chương 6, tác giả giải thích về phương pháp tìm kiếm khoảng tin cậy bằng cách so sánh với cách chúng ta tìm kiếm đồ thất lạc trong nhà. Những so sánh này giúp cho người đọc dễ dàng hình dung và hiểu rõ hơn về các khái niệm và phương pháp thống kê.
Ngoài ra, cuốn sách cũng đưa ra nhiều lời khuyên và kinh nghiệm trong việc áp dụng thống kê vào thực tiễn. Tác giả giải thích rất chi tiết về các bước tiếp cận và phân tích dữ liệu, cũng như các lỗi thường gặp khi áp dụng thống kê và cách khắc phục chúng.
Cuối cùng, đây là một cuốn sách thực sự rất thú vị và dễ hiểu. Tác giả đã thực sự thành công trong việc giải thích các khái niệm và phương pháp thống kê một cách rõ ràng và dễ hiểu, bằng cách sử dụng những hình ảnh hoạt hình vui nhộn và ví dụ thực tế. Cuốn sách cũng cung cấp cho người đọc nhiều kinh nghiệm và lời khuyên về cách áp dụng thống kê vào thực tiễn. Nếu bạn muốn tìm hiểu về thống kê một cách thú vị và dễ hiểu, "The Cartoon Guide to Statistics" là một lựa chọn tuyệt vời cho bạn.
Mình mua sách gốc cuốn này tại Bookee, bạn cần mua thì có thể tham khảo ở đây: https://bookee.store/the-cartoon-guid...
July 4, 2023
This was a surprisingly great overview of statistics from summary stats to counting to probability to distributions, confident intervals, hypothesis testing/experiment design, and regressions. Some examples were really illustrative and overall the comics were meh but still fun to have.
Quotes:
- "Data analysis: the gathering, display and summary of data. Probability: the laws of chance, in and out of the casino. Statistical inference: the science of drawing statistical conclusions from a specific data, using a knowledge of probability."
- "Unlike the IQR [interquartile range], which is based on medians, the standard deviation measures the spread from the mean. You can think of it, roughly speaking, as the average distance of the data from the mean x. Except that we use the squares of the distance instead....a spread measure should have the same units as the original data.... the obvious thing to do is to take the square root.... the standard deviation."
- "The simple random sample has two properties that make it the standard against which we measure all other methods: [1] Unbiased: each unit has the same chance of being chosen, [2] Independence: selection of one unit has no influence on the selection of other units."
- "[Central Limit Theorem] what is remarkable about this? It says that regardless of the shape of the original distribution... the taking of averages results in a normal. To find the distribution of x, we need know only the population mean and standard deviation..... the three probability densities above all have the same mean and standard deviation. Despite their different shapes, when n=10, the sampling distributions of the mean, x, are nearly identical."
- "In scientific work, a fixed alpha level of .05 or .01 is often used. These fixed levels are a holdover artifact from the pre-computer era, when we had to refer to tables, which were printed only for selected critical values. Still, many scientific journals continue to publish results only when the p-value is <= 0.05"
- "This is what is called a type I error: an alarm without a fire. Conversely, a type II error is a fire without an alarm. Every cook knows how to avoid a type I error: just remove the batteries. Unfortunately, this increases the incidences of type II errors!"
- "Data quality. Seemingly small errors in sampling, measurement, and data recording can play havoc on any analysis. R.A. Fisher, geneticist and founder of modern statistics, not only designed and analyzed animal breeding experiments, he also cleaned the cages and tended the animals, because he know that the loss of an animal would influence his results."
Quotes:
- "Data analysis: the gathering, display and summary of data. Probability: the laws of chance, in and out of the casino. Statistical inference: the science of drawing statistical conclusions from a specific data, using a knowledge of probability."
- "Unlike the IQR [interquartile range], which is based on medians, the standard deviation measures the spread from the mean. You can think of it, roughly speaking, as the average distance of the data from the mean x. Except that we use the squares of the distance instead....a spread measure should have the same units as the original data.... the obvious thing to do is to take the square root.... the standard deviation."
- "The simple random sample has two properties that make it the standard against which we measure all other methods: [1] Unbiased: each unit has the same chance of being chosen, [2] Independence: selection of one unit has no influence on the selection of other units."
- "[Central Limit Theorem] what is remarkable about this? It says that regardless of the shape of the original distribution... the taking of averages results in a normal. To find the distribution of x, we need know only the population mean and standard deviation..... the three probability densities above all have the same mean and standard deviation. Despite their different shapes, when n=10, the sampling distributions of the mean, x, are nearly identical."
- "In scientific work, a fixed alpha level of .05 or .01 is often used. These fixed levels are a holdover artifact from the pre-computer era, when we had to refer to tables, which were printed only for selected critical values. Still, many scientific journals continue to publish results only when the p-value is <= 0.05"
- "This is what is called a type I error: an alarm without a fire. Conversely, a type II error is a fire without an alarm. Every cook knows how to avoid a type I error: just remove the batteries. Unfortunately, this increases the incidences of type II errors!"
- "Data quality. Seemingly small errors in sampling, measurement, and data recording can play havoc on any analysis. R.A. Fisher, geneticist and founder of modern statistics, not only designed and analyzed animal breeding experiments, he also cleaned the cages and tended the animals, because he know that the loss of an animal would influence his results."
November 28, 2017
This book must have been in the periphery of my consciousness when I was growing up, because the idea of reading it suddenly manifested itself fully-formed to me one day after, as a math teacher whose weak point is statistics, I'd long nursed a need to get better at the subject: "Wait a minute, isn't there some kind of Cartoon Guide to Statistics or something?" And yes, I could have looked at an actual statistics textbook; but, the point of this book, it seems to me, is to be more human than that. To be readable on its own, rather than as a reference for a course. Sure: I've heard tell of many students, whether totally self-motivated or, if not, still about as serious about math as I sometimes imagine myself becoming, working their way through advanced textbooks, completing all the exercises. ... And are those the same people who review said textbooks, and celebrate their density as admirable lack of hand-holding? Let me not cast my lot in, in any case, with those folks. No, the Cartoon Guide to Statistics isn't a textbook, but it's something I can look at fondly, a complement to a lifestyle, a collection of (cartoon) points of reference. I look forward to returning later on to this book and saying to myself, finally, "Aha! That's the connection they were making, all those years ago!"
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