# Is this a Formal (Statistical) Fallacy?

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• WWGD
In summary, the fallacy discussed in the conversation is based on looking at a non-representative subsample of the population, leading to the conclusion that a variable has no effect on other variables of interest. This fallacy does not have a specific name, but it can be related to survivorship bias and fallacies of composition and biased statistics.
WWGD
Gold Member
TL;DR Summary
Fallacy on a supposed falsification of a correlation by looking at a subset of the population.
Hi All,
I have recently read about a fallacy that seems to be based on looking at a non-representative subsample of the population. I would like to know if this goes by a name and if it has been formalized. It just seems the problem is that of considering a variable within a subpopulation and not within the whole population ( here population referring to actual people). It is then concluded that the variable in question has no effect on other variables of interest.

Here are two examples:
1) The GRE test is artificial, without any real predictive power: It does not correlate with GPA in graduate school, nor with other measures of success. But, when considered within the population of , say, adults, a high GRE test does correlate highly with variables as income, job satisfaction, etc.

2) Like in 1), we can argue that height does not highly correlate with the ability to score: even as NBA players differ in several inches in height, their total points totals are the same

3)Hand-eye coordination and batting average in Baseball. Say we could find numerical measures. These do not correlate with batting average, WAR or slugging average.

Does this fallacy have a name? Is it based on other than just considering a subsample, a sort of survivorship bias?

It isn't the same thing per se, but this feels 'close' to Berkson's Paradox

WWGD

## What is a formal (statistical) fallacy?

A formal (statistical) fallacy is a type of logical error that occurs in statistical reasoning. It is a mistake in the structure or form of an argument, rather than the content. These fallacies can lead to incorrect conclusions or misleading interpretations of data.

## How can I identify a formal (statistical) fallacy?

Formal (statistical) fallacies can be identified by examining the structure of an argument and looking for any errors in logic. Common types of formal fallacies include affirming the consequent, denying the antecedent, and false dilemma. It is important to be aware of these fallacies in order to avoid making incorrect conclusions based on statistical data.

## What are some examples of formal (statistical) fallacies?

One example of a formal (statistical) fallacy is the gambler's fallacy, which is the mistaken belief that if a certain event has not occurred for a while, it is more likely to occur in the future. Another example is the correlation-causation fallacy, which assumes that just because two variables are correlated, one must cause the other.

## How can I avoid making formal (statistical) fallacies?

To avoid making formal (statistical) fallacies, it is important to critically evaluate the structure of an argument and look for any logical errors. It can also be helpful to consult with experts or use statistical software to ensure accurate analysis and interpretation of data.

## Why is it important to understand formal (statistical) fallacies?

Understanding formal (statistical) fallacies is important for making informed decisions based on statistical data. By being aware of these fallacies, we can avoid making incorrect conclusions and ensure that our arguments and reasoning are sound. This is especially crucial in fields such as science, where accurate data analysis is essential for making scientific advancements.

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