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Logical Fallacy of Biased Statistics / Biased Statistics / Loaded Sample / Prejudiced Statistics / Prejudiced sample / Loaded Statistics / Biased Induction / Biased Generalization / Unrepresentative Sample / Unrepresentative Generalization

 

Logical Fallacy of Biased Statistics / Biased Statistics / Loaded Sample / Prejudiced Statistics / Prejudiced sample / Loaded Statistics / Biased Induction / Biased Generalization / Unrepresentative Sample / Unrepresentative Generalization

Fallacy of biased statistics is one of the many smokescreens that are used to cover the fact that the reasoning is based on one of the three fallacies of Agrippa's trilemma. Whenever a logical fallacy is committed, the fallacy has its roots in Agrippa's trilemma. All human thought (without Divine revelation) is based on one of three unhappy possibilities. These three possibilities are infinite regress, circular reasoning, or axiomatic thinking. This problem is known as Agrippa's trilemma. Some have claimed that only logic and math can be known without Divine revelation; however, that is not true. There is no reason to trust either logic or math without Divine revelation. Science is also limited to the pragmatic because of the weakness on human reasoning, which is known as Agrippa's trilemma.

The logical fallacy of biased statistics occurs when a generalization is made based on statistics that are inadequate for the generalization to be known. As with all fallacies, the person who is fooling others with the fallacy may or may not be fooled by the fallacy as well.

Examples of the Logical Fallacy of Biased Statistics / Biased Statistics / Loaded Sample / Prejudiced Statistics / Prejudiced sample / Loaded Statistics / Biased Induction / Biased Generalization / Unrepresentative Sample / Unrepresentative Generalization

“George W. Bush could not possibly have been elected fairly. I don’t know a single person who voted for him!”

Friends tend to select friends who are like-minded. This is a very biased sample.

The Kinsey Report

The Kinsey Report was based on volunteer bias, disproportionate sampling from some groups (strata) with high rates, inappropriate analytical methods, misleading definitions, and possibly, intentional fraud due to personal bias. Yet it has impacted U.S. morality, laws, and thinking dramatically.

“Tea Party members and conservatives don’t understand science. Every conservative I know is ignorant of scientific method.”

This remark asserts something that is statistical in nature. The person making the remark is not basing it on sound research. This has been tested by a liberal professor, Dan Kahan, who, through a government-funded grant, did a study of Tea Party members as compared with non-Tea Party members. Kahan was surprised to find that the base rate puts Tea Party members measurably higher in their understanding of science. http://www.ijreview.com/2013/10/87474-yale-professors-surprising-discovery-tea-party-supporters-scientifically-literate/

“Home schooled children and children who don’t attend public schools are disadvantaged when they go to college. The neighbor’s kid who went to Catholic school didn’t even  go to college.”

The statistics don’t support this. In an extreme case, someone may even say that they don’t care about the statistics and stick to their guns on the issue.

Polls are often cleverly worded to get a certain result so that they can be used to mold public opinion.

Figures don’t lie, but liars figure.



Author/Compiler
Last updated: Sep, 2014
 
 




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Logical Fallacy of Misused Statistics

Misunderstanding the Nature of Statistics / Innumeracy

Clustering Illusion

Logical Fallacy of Bad Statistical Data

Logical Fallacy of Biased Statistical Method

Logical Fallacy of Biased Statistical Calculation

Logical Fallacy of Biased Conclusion from Statistics

Logical Fallacy of Biased Reporting of Statistics

Logical Fallacy of Biased Statistics / Biased Statistics / Loaded Sample / Prejudiced Statistics / Prejudiced sample / Loaded Statistics / Biased Induction / Biased Generalization / Unrepresentative Sample / Unrepresentative Generalization

Logical Fallacy of Generalizing from a Hypostatization

Logical Fallacy of Error in Sampling

Logical Fallacy of Avoiding Specific Numbers

Logical Fallacy of Fake Precision / Over Precision / False Precision / Misplaced Precision / Spurious Accuracy

Logical Fallacy of Self-Selected Biased Sample

Logical Fallacy of Comparing Two Things Statistically that are not Technically Comparable / Statistical Apples and Oranges

Logical Fallacy of Ludus / Ludic Fallacy

Logical Fallacy of Fishing for Data / Data Dredging / Data Fishing / Data Snooping / Equation Fitting

Logical Fallacy of Base Rate Neglect / Base Rate Fallacy / Neglecting Base Rates / Base Rate Bias / Prosecutor's Fallacy

Logical Fallacy of Isolated Examples

Logical Fallacy of Hasty Generalization / False Generalization / Glittering Generalities

Logical Fallacy of Argument from Small Numbers / Small Sample Size Bias

General Rule Fallacy

Logical Fallacy of Specificity

Logical Fallacy of Overwhelming Exception

Logical Fallacy of Stereotyping

Logical Fallacy of Dicto Simpliciter / Sweeping Generalization

Gambler's Fallacy / The Monte Carlo Fallacy / The Doctrine The Maturity of Chances / Hot Hand Fallacy

Logical Fallacy of Appeal to Possibility / Appeal to Probability

Logical Fallacy of Appeal to Infinite Possibilities

Texas Sharpshooter Fallacy

Misuse of Averages Fallacy


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