How To Lie With Statistics Summary

Terms in this set (10)

Response Bias: Tendency for people to over- or under-state the truth
Non-response: People who complete surveys are systematically different from those who fail to respond. Accessibility/Pride.
Representative Sample: One where all sources of bias have been removed. (Literary Digest)
Questionnaire wording/Interviewer effects
Recall Bias: Tendency for one group to remember prior exposure in retrospective studies
The sample with a built-in bias : the origin of the statistics problems - the sample. Any statistic is based on some sample (because the whole population can't be tested) and every sample has some sort of bias, even if the person wanting the statistic tries hard to not create any. The built-in bias comes from the respondents not replying honestly, the market researcher picking a sample that gives better numbers, personal biases based on the respondent's perception of the market researcher, data not being available at a certain past time are a few of the biases that creep in when building a statistic. One of the example (from the 1950s) that the author mentions is a readership survey of two magazines. Respondents were asked which magazine they read the most - Harpers or True love story. Most respondents came back that they read the True Love Story, but that publisher's figures came back that the True Love Story had a much higher circulation than Harpers - refuting the results from the sampling. The reason for this discrepancy - people were not willing to respond due to their own bias. As Dr.House says - Everybody Lies ! Summary of the chapter - given any statistic, question the sample that was taken. Assume that there is always a bias in the sample
Arithmetic Mean: Evenly distributes the total among individuals. Can be unrepresentative when measurements are highly skewed right. (e.g. per capita income)
Median: Value dividing distribution into two equal parts. 50th percentile. (e.g. median household income)
Mode: Most frequently observed outcome (rarely reported with numeric data)
The well-chosen average: how not qualifying an average can change the meaning of the data. Before I delve into this, quickly, when I say, average - what comes to your mind? Sum(x1....xn) / N - right? The arithmetic mean. But I said average, not arithmetic average did I? Not many people know that there are 3 averages
Arithmetic average / mean - sum of quantities / number of quantities
Median - the middle point of the data which separates the data, the midpoint when data is sorted
Mode - the data point that occurs the most in a given set of data
And when someone says average, leaving it unqualified, there is a lot of room for juggling. The author mentions a very simple example. If an organization publishes a statistic that the average pay of the employees is $1000, what does this mean? This makes most of us think that almost everyone makes around $2000 - the reader thinks it is the median. But, the corporation can be talking about an arithmetic mean, where the boss might be earning say $10,500 and the rest of the 19 employees earn $500 each - the arithmetic average. Just by not qualifying the average the published fact can be completely twisted out of form from the real facts.The way out - always ask what is the kind of the average that someone is talking about.
Small samples: Estimators with large standard errors, can provide seemingly very strong effects
Low incidence rates: Need very large samples for meaningful estimates of low frequency events
Significance levels/margins of error: Measures of the strength and precision of inference
Ranges: Report ranges or standard deviations along with means (e.g. "normal" ranges)
Inferring among individuals versus populations
Clearly label chart axes
The little figures that are not there: This chapter is about how the sample data is picked up in a way to prove the results - something we are all too aware in marketing campaigns. And picking the sample data right can mean picking a sample size that gives the kind of results we are looking for or a smaller number of trials. The author demonstrates this with a very important issue for parents - is my normal or not. The author talks about the 'Gesell Norms', where Dr.Arnold Gesell stated that most kids sit erect by the age of two. This immediately translates to a parent trying to think about his/her kid and deciding whether the kid is normal or not. What is missing in this case is, that, from the source of the information (the research) to the Sunday paper where a parent read this, the average has been changed from a range to an exact figure. If the writer of the Sunday magazine article mentioned to the reader that there is a range of age in which a child sits erect, the reader is assuaged and that is where the little figures disappear. The way out - ask if the information presented is a discrete quantity or if there is a range involved.
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