A quick take on how picking non-random data skews results and creates systematic errors. A classic 1936 polling failure is used to show how excluded groups warp predictions. A contrast with a smaller, truly random poll highlights the difference sampling methods make.
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insights INSIGHT
How Sample Selection Bias Distorts Results
Sample selection bias arises when data chosen for analysis is non-random and systematically flawed.
Excluding subsets by attribute, technique, or location skews results and significance.
question_answer ANECDOTE
The 1936 Polling Failure
The 1936 Literary Digest poll predicted Alf Landon would beat Franklin Roosevelt using 2 million mailed surveys.
The sample over-represented wealthy car and phone owners, producing a misleading result corrected by Gallup's smaller, random poll.
insights INSIGHT
Size Doesn’t Fix A Biased Sample
Larger sample size cannot fix biased sampling if the selection process over-represents certain groups.
George Gallup's smaller, properly selected poll predicted the election accurately despite fewer respondents.
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In this episode, we are considering the Sample Selection Bias. Sample Selection Bias is a systematic error caused by choosing non-random data for statistical and qualitative analysis. The bias exists due to a flaw in the sample selection process, where a subset of the data is excluded due to a particular attribute, sampling technique or even geographic location; resulting in a biased sample, defined as a statistical sample of a population in which all participants are not equally balanced or objectively represented. The exclusion of the subset can influence the statistical significance and produces distorted results.
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