How To Lie With Statistics Summary
1. The Sample with the Built-in Bias - (correct Answer) - 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
2. The Well-Chosen Average - (correct Answer) - 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
1. The Sample with the Built-in Bias - (correct Answer) - 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
2. The Well-Chosen Average - (correct Answer) - 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
