Meaning from Data: Statistics Made Clear

Course No. 1487
Professor Michael Starbird, Ph.D.
The University of Texas at Austin
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Course No. 1487
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Course Overview

Who was the greatest baseball hitter of all time? How likely is it that a poll is correct? Is it smart to buy last year's highest-performing stock? Which hospital has the best outcome for a given procedure? When is it a good idea to buy a product's extended warranty?

These questions all involve the interpretation of statistics, as do a surprising number of other mysteries, including: Is the "hot hand" among sports players real? How can you tell if Shakespeare is the probable author of a newly discovered poem? What is a guilt-free way to get someone to admit to cheating? And, how does a tax assessor calculate the market value of a house?

Meaning from Data: Statistics Made Clear is your introduction to a vitally important subject in today's data-driven society. In 24 half-hour lectures, you will explore the principles and methods that underlie the study of statistics. You have probably heard such terms as mean, median, percentile, quartile, statistically significant, and bell curve, and you may have a rough idea of what they mean. This course sharpens your understanding of these and scores of other statistical concepts and shows how, properly used, they can extract meaning from data.

Become Statistically Savvy

These challenging yet accessible lectures assume no background in mathematics beyond basic algebra. While most introductory college statistics courses stress technical problem solving and plugging data into formulae, this course focuses on the logical foundations and underlying strategies of statistical reasoning, illustrated with plenty of examples. Professor Michael Starbird walks you through the most important equations, but his emphasis is on the role of statistics in daily life, giving you a broad overview of how statistical tools are employed in risk assessment, college admissions, drug testing, fraud investigation, and a host of other applications.

Statistical Adventures

Professor Starbird is a master at conveying concepts through examples. Some of these include:

  • When is a Lottery not a Lottery? When it is not truly random. The 1969 Vietnam War draft lottery assigned young draft-age men a ranking for induction based on their birthdays, which were placed in capsules and drawn from a container, supposedly at random. But by computing the statistical correlation for the order-of-draw, it's clear that a nonrandom variable was at play. The most likely explanation is that the capsules with the dates were not thoroughly mixed.
  • The Birthday Challenge: What is the probability that out of 50 random people, two of them share the same birthday? The chances are much higher than most people think.
  • The Chicken Soup Method: How can 1,000 randomly chosen people serve as a predictor for the behavior of hundreds of millions of voters? This is the essence of a political poll, and its effectiveness should be no more surprising than the fact that that a single taste of chicken soup is enough to predict the overall saltiness of the batch, whether the batch is in a cup or a giant vat.
  • Beware of Fallacious Reasoning: At the O. J. Simpson murder trial, Simpson's lawyer Johnnie Cochran countered evidence that Simpson had beat his wife with a statistic that only 1 in 1,000 wife beaters go on to kill their wives. Therefore, Cochran argued, there was only a 1 in 1,000 chance that Simpson went on to commit the murder. Professor Starbird discusses the fallacies in this argument, including the fact that a wife was actually murdered in this case, so the relevant question should be: What is the probability that she had previously been beaten?
  • Who Really Won the 1860 Presidential Election? Establishing the will of the people in an election can be tricky, especially when three or more candidates are involved. Professor Starbird shows how the results of the four-way presidential race of 1860 can be interpreted as giving victory to each of three candidates, depending on the voting scheme employed. Abraham Lincoln won according to the rules in place, but given other equally valid rules, the victor—and history—would have been very different.

Statistics Is Everywhere

Statistical information is truly everywhere. You can't look at a newspaper without seeing statistics on virtually every page. You can't talk about the weather forecast without invoking statistics. Statistics obviously arises in business and social science but even enters the arts in analyzing manuscripts. And you'd better not go to a casino without understanding statistics. "It's really harder to find somewhere where statistics isn't important than to find the places where it is," says Professor Starbird.

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24 lectures
 |  Average 30 minutes each
  • 1
    Describing Data and Inferring Meaning
    The statistical study of data deals with two fundamental questions: How can we describe and understand a situation when we have all the pertinent data about it? How can we infer features of all the data when we know only some of the data? x
  • 2
    Data and Distributions—Getting the Picture
    The first three rules of statistics should be: Draw a picture, draw a picture, draw a picture. A visual representation of data reveals patterns and relationships, for example, the distribution of one variable, or an association between two variables. x
  • 3
    Inference—How Close? How Confident?
    The logic of statistical inference is to compare data that we collect to expectations about what the data would be if the world were random in some particular respect. Randomness and probability are the cornerstones of all methods for testing hypotheses. x
  • 4
    Describing Dispersion or Measuring Spread
    This lecture defines and explores standard deviation, which measures how widely data are spread from the mean. The various methods of measuring data dispersion have different properties that determine the best method to use. x
  • 5
    Models of Distributions—Shapely Families
    Any shaped curve can model a data set. This lecture looks at skewed and bimodal shapes, and describes other characteristically shaped classes of distributions, including exponential and Poisson. Each shape arises naturally in specific settings. x
  • 6
    The Bell Curve
    The most famous shape of distributions is the bell-shaped curve, also called a normal curve or a Gaussian distribution. This lecture explores its properties and why it arises so frequently—as in the central limit theorem, one of the core insights on which statistical inference is based. x
  • 7
    Correlation and Regression—Moving Together
    One way we attempt to understand the world is to identify cases of cause and effect. In statistics, the challenge is to describe and measure the relationship between two variables, for example, incoming SAT scores and college grade point averages. x
  • 8
    Probability—Workhorse for Inference
    Probability accomplishes the seemingly impossible feat of putting a useful, numerical value on the likelihood of random events. Our intuition about what to expect from randomness is often far from accurate. This lecture looks at several examples that place intuition and reality far apart. x
  • 9
    Samples—The Few, The Chosen
    Sampling is a technique for inferring features of a whole population from information about some of its members. A familiar example is a political poll. Interesting issues and problems arise in taking and using samples. Examples of potential pitfalls are explored. x
  • 10
    Hypothesis Testing—Innocent Until
    This lecture introduces a fundamental strategy of statistical inference called hypothesis testing. The method involves assessing whether observed data are consistent with a claim about the population in order to determine whether the claim might be false. Drug testing is a common application. x
  • 11
    Confidence Intervals—How Close? How Sure?
    Headlines at election time frequently trumpet statistics such as: "Candidate A will receive 59 percent of the vote, with a margin of error of plus or minus 3 percent." This lecture investigates what this "margin of error" statement means and why it is incomplete as written. x
  • 12
    Design of Experiments—Thinking Ahead
    When gathering data from which deductions can be drawn confidently, it's important to think ahead. Double-blind experiments and other strategies can help meet the goal of good experimental design. x
  • 13
    Law—You’re the Jury
    Opening the second part of the course, which deals with applying statistics, this lecture focuses on two examples of courtroom drama: a hit-and-run accident and a gender-discrimination case. In both, the analysis of statistics aids in reaching a fair verdict. x
  • 14
    Democracy and Arrow’s Impossibility Theorem
    An election assembles individual opinions into one societal decision. This lecture considers a surprising reality about elections: The outcome may have less to do with voters' preferences than with the voting method used, especially when three or more candidates are involved. x
  • 15
    Election Problems and Engine Failure
    The challenge of choosing an election winner can be thought of as taking voters' rank orderings of candidates and returning a societal rank ordering. A mathematically similar situation occurs when trying to determine what type of engine lasts longest among competing versions. x
  • 16
    Sports—Who’s Best of All Time?
    Analyzing statistical data in sports is a sport of its own. This lecture asks, "Who is the best hitter in baseball history?" The question presents statistical challenges in comparing performances in different eras. Another mystery is also probed: "Is the 'hot hand' phenomenon real, or is it random?" x
  • 17
    Risk—War and Insurance
    A discussion of strategies for estimating the number of Mark V tanks produced by the Germans in World War II brings up the idea of expected value, a central concept in the risky business of buying and selling insurance. x
  • 18
    Real Estate—Accounting for Value
    Tax authorities often need to set valuations for every house in a tax district. The challenge is to use the data about recently sold houses to assess the values of all the houses. This classic example of statistical inference introduces the idea of multiple linear regression. x
  • 19
    Misleading, Distorting, and Lying
    Statistics can be used to deceive as well as enlighten. This lecture explores deceptive practices such as concealing lurking variables, using biased samples, focusing on rare events, reporting handpicked data, extrapolating trends unrealistically, and confusing correlation with causation. x
  • 20
    Social Science—Parsing Personalities
    This lecture addresses two topics that come up when applying statistics to social sciences: factor analysis, which seeks to identify underlying factors that explain correlation among a larger group of measured quantities, and possible limitations of hypothesis testing. x
  • 21
    Quack Medicine, Good Hospitals, and Dieting
    Medical treatments are commonly based on statistical studies. Aspects to consider in contemplating treatment include the characteristics of the study group and the difference between correlation and causation. Another statistical concept, regression to the mean, explains why quack medicines can appear to work. x
  • 22
    Economics—“One” Way to Find Fraud
    Economics relies on a wealth of statistical data, including income levels, the balance of trade, the deficit, the stock market, and the consumer price index. A surprising result of such data is that the leading digits of numbers do not occur with equal frequency, and that provides a statistical method for detecting fraud. x
  • 23
    Science—Mendel’s Too-Good Peas
    Statistics is essential in sciences from weather forecasting to quantum physics. This lecture discusses the statistics-based research of Johannes Kepler, Edwin Hubble, and Gregor Mendel. In Mendel's case, statisticians have looked at his studies of the genetics of pea plants and discovered data that are too good to be true. x
  • 24
    Statistics Everywhere
    The importance of statistics will only increase as greater computer speed and capacity make dealing with ever-larger data sets possible. It has limits that need to be respected, but its potential for helping us find meaning in our data-driven world is enormous and growing. x

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Your professor

Michael Starbird

About Your Professor

Michael Starbird, Ph.D.
The University of Texas at Austin
Dr. Michael Starbird is Professor of Mathematics and University Distinguished Teaching Professor at The University of Texas at Austin, where he has been teaching since 1974. He received his B.A. from Pomona College in 1970 and his Ph.D. in Mathematics from the University of Wisconsin-Madison in 1974. Professor Starbird's textbook, The Heart of Mathematics: An Invitation to Effective Thinking, coauthored with Edward B. Burger,...
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Meaning from Data: Statistics Made Clear is rated 3.9 out of 5 by 64.
Rated 5 out of 5 by from A very nice introduction I lent this to a friend of mine who's studying to become a teacher. She's had a lot of experience tutoring mathematics and is very good at it. She knew the material, but was fascinated, and inspired, by how well it was presented in this course. My own response was the same. Well done.
Date published: 2009-06-14
Rated 4 out of 5 by from Good overview Prof Starbird uses a range of excellent examples of how statistics are applied to a variety of problems. I would have liked to have seen a fuller explanation of the Normal Distribution and the Chi Squared distributions, as these under pinned a lot of the practical examples. Overall a worthwhile course and well presented (Prof Starbird needs a set of new jokes though).
Date published: 2009-06-04
Rated 5 out of 5 by from Quite useful Though I use statistics often in my job, reliance on macros to do the math for me made me lazy...and as the trite but true saying goes, "what you don't use, you lose." This series was wonderful in foundation and presentation. Not only did it reawaken the concepts, but has inspired me to do the math myself (when I have enough time) rather than letting the computer have all the fun. Very recommended.
Date published: 2009-02-27
Rated 4 out of 5 by from Fear itself??? Stats Statistics, The very mention of this word conjures up fear in the minds of many collage students.Its a required course in almost every field at conventional Universities. One of the main obstacles for a successful experience is overcoming the intimidation factor. I certainly dreaded my eventual semester when this intimidating obstacle would lay before me and my diploma. I made a decision to turn to the Teaching Company for some help in order to face my fear and fulfill this requirement for graduation.Professor Starbird introduction was extremely disarming and seemed to be aware of the "intimidation Factor" that surrounds statistics and puts the student at ease almost immediately.His uncanny ability to simplify even the most convoluted lengthy process was the elixir my confidence required to see the important tool of STATISTICS for what it is.I not only passed the course but understood the concepts and used them in other classes as a research tool.within my arguments.I wouldn't say I'M passionate about statistics but it certainly granted me vision to excel within fields where my passion exists.
Date published: 2009-02-21
Rated 5 out of 5 by from Good examples The first half is a bit dry but those lectures are essential. The second half is very interesting.
Date published: 2009-02-20
Rated 5 out of 5 by from Useful and entertaining It's not easy to use the words "useful" and "entertaining" in the same sentence when you're thinking about statistics, but this course if definitely both. Dr. Starbird is a professor in the literal sense of the word: He both explains and advocates the subject. This is an excellent survey of the subject of statistics. It would be a very good introduction for novices and an excellent review for those with more background. It would also be a soothing restorative for those whose statistics courses left them puzzled or dazed and confused. Dr. Starbird's great strength as an instructor is his skill in selecting and explaining examples from ordinary experience to illustrate the concepts he's discussing in each lecture. The result is understanding and also enjoyment.
Date published: 2009-02-12
Rated 5 out of 5 by from Helped in a weak area Science and Mathematics are my weakest areas, so I was looking for a course that provided an overview with rudimentary knowledge, and a tease for some more slightly elevated material, Professor Starbird fulfilled those objectives. He is an excellent instructor who brings a great deal of enthusiasm to the content, uses analogies well, and proceeds with logical, coherent steps from concept to concept. For those with more command of mathematics, this may be a little too basic, and perhaps the TeachCo. can provide a more advanced course; but for the general knowledge person, who just wants a grasp of the topic, this is highly recommended.
Date published: 2009-02-07
Rated 4 out of 5 by from Enthusiasm for statistics I agree with other reviewers that this course prepares you to take a "real" statistics course (where you have to do all the math and solve the problems). It does this by covering statistics at the conceptual level without delving much into the math. I personally liked the second half of the course better than the first half. If you already have some knowledge of stats and feel like the course is going too slow, skim the notes and jump to lecture 13. Prof Starbird does a great job of being enthusiastic about a topic that other teachers can make too dry.
Date published: 2009-01-12
Rated 5 out of 5 by from For Best Use ... There seems to be an expectation-gap in TTC courses, resulting in some disappointment. TTC courses are designed to take you up to, but not through, the problem-solving level. Rather, they look at the subject "from above", to give you an understanding and perspective on the major issues and concepts - what the subject is all about, what's important and why, historical perspectives, etc - and to prepare you for a more formal, problem-solving course, should you decide that's where you want to go. You might not think it possible to discuss a math-related subject such as statistics using only minimum math (and even that being optional to the understanding), but that's just what this course does, and it does it excellently. What optional math is presented is understandable using only high-school algebra. Although there is one short section which uses some calculus, it's not necessary for understanding the issue. This course is nicely laid out, with the first half on the concepts of statistics and the processes we use to coerce data into meaningful representations, and the second half on the real-life applications of statistics. As a teacher, Prof. Starbird does an excellent job. I very much enjoyed these lectures. Hints: before looking at a lecture, and after, read the brief section in the course outline. If you need to take a lecture a second time, do it. Additionally, you can take the course a second time, taking notes. Once you "get the picture", NOW you're completely prepared for problem-solving. If you are looking for a conceptual, high-level understanding of the subject of statistics, this course will do it for you. If you are looking to take a more formal, problem-solving course in statistics, taking this course first will increase your productivity in that course, and just might be reflected in your grades. Use this course within its objectives, and you won't be disappointed at all.
Date published: 2009-01-02
Rated 4 out of 5 by from Pretty Good, But Could Be Better This course is generally pretty good, but it's somewhat of a mixed bag. On the negative side, the first half of the course presents basic concepts of statistics, and I found the presentation too dumbed down. It's fine to emphasize concepts, but this is a math course after all, so we need to see considerably more detailed math, including formulas. On the positive side, the second half of the course emphasizes applications, and this was well done, with many of the examples being quite interesting and sometimes even counterintuitively surprising. Starbird is also a very personable lecturer with a down to earth style, and that certainly helps. Overall, I recommend the course for those with an interest in statistics, not too much prior background in the subject, and not expecting more than a very basic introduction. For those with more background or looking for a more extensive introduction, you may find the course too limited for your needs, and thus a course to skip.
Date published: 2008-12-21
Rated 5 out of 5 by from The best beginners course in statistics I 'm a math phobic. I have somehow struggled through my math classes in college with high Bs but have never felt comfortable with subject. My plan is to get a Masters in Social Work and I was sick of being afraid of math and statistics. This set of CDs introduces you to the subject of statistics. The lectures are taught by Professor Starbird and have been the best experience that I have ever had in learning math of any kind. It is also an excellent overview for those of us who are starting at the bottom in our understanding of the subject. I don't agree with the other reviewers who said that this course is somehow lacking in anything. The problem is that they already have some knowledge about the subject and are really looking for a more advanced course. For the beginner I can't say enough about this set. If you need to know statistics I highly recommend these lectures from Professor Starbird. I like this company so much I bought all of their math courses so that I can use them to brush up for the GRE. I haven't watched them yet so I can't comment but I feel confident that they will be just as good as this CD set was.
Date published: 2008-10-25
Rated 2 out of 5 by from The lecturer was poor. He was not presenting a course in statistics, he was just giving a course overview of statistics.
Date published: 2008-10-17
Rated 4 out of 5 by from A little more on outliers & extremes would have been helpful. sometimes statistics of extremes is precisely what's of interest - floods, for example.
Date published: 2008-10-17
Rated 4 out of 5 by from Prof. Starbird takes the confusion out of learning statistics and presents an informative and delightful course.
Date published: 2008-10-17
Rated 4 out of 5 by from I expected a little more "how to" including math in the course.
Date published: 2008-10-17
Rated 4 out of 5 by from Dr. starbird made a difficult subject more approachable &comprehensible.
Date published: 2008-10-17
Rated 4 out of 5 by from A must-do workout for one's brain/mind/intellect. Highly recommended.
Date published: 2008-10-17
Rated 5 out of 5 by from Dr. Starbird did an excellent job of giving the historical background and including those who were less enthusiastic about statistics like Jung. Excel was mentioned multiple times which is good because I use Excel every day to analyze measurements.
Date published: 2008-10-17
Rated 5 out of 5 by from Professor Starbird is an outstanding teacher. He gives practical examples. He makes a difficult subject more enjoyable by adding humor.
Date published: 2008-10-17
Rated 5 out of 5 by from This course was better than my phd level course in statistics!
Date published: 2008-10-17
Rated 5 out of 5 by from Outstanding lectures by professor Starbird.
Date published: 2008-10-17
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