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Pseudocertainty Effect: Definition, Examples and Effects

Insensitivity To Sample Size is a cognitive bias that affects how people interpret data. It occurs when people overestimate the importance of a sample size, regardless of its actual size. This bias can lead to incorrect conclusions and decisions.


Definition: Insensitivity To Sample Size is a cognitive bias that occurs when people overestimate the importance of a sample size, regardless of its actual size. This bias can lead to incorrect conclusions and decisions.


Examples:

1. A survey of 100 people is conducted to determine the opinion of a population of 1 million people. The results of the survey are taken to be representative of the entire population, even though the sample size is too small to be accurate.

2. A study of 10 people is conducted to determine the effectiveness of a new drug. The results of the study are taken to be representative of the entire population, even though the sample size is too small to be accurate.


Effects: Insensitivity To Sample Size can lead to incorrect conclusions and decisions. It can lead to inaccurate predictions and assumptions about a population, which can have serious consequences. For example, if a survey of 100 people is used to determine the opinion of a population of 1 million people, the results may be misleading and lead to incorrect decisions. Additionally, it can lead to a lack of trust in data and research, as people may not believe the results if the sample size is too small.


Do you want to expand your knowledge on this topic? Read our full in-depth article on cognitive biases.


Do you have extra 15 minutes today? Takeour fun and interactive quiz to learn which of 16 reasoning styles you use, your overall level of rationality, and what you can do now to improve your rationality skills.

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