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Related Concept Videos

P-value01:10

P-value

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P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
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Decision Making: P-value Method01:09

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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Fisher's Exact Test01:08

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Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of...
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Statistical Significance01:50

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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Categorical Perception of p-Values.

V N Vimal Rao1, Jeffrey K Bye1, Sashank Varma2

  • 1Department of Educational Psychology, University of Minnesota.

Topics in Cognitive Science
|November 15, 2021
PubMed
Summary

Statistical training creates a mental boundary at the .05 significance level, affecting researchers' p-value interpretation. This dichotomization distorts probability perception, contrary to modern statistical reasoning.

Keywords:
Categorical perceptionProbabilistic reasoningRational number processingStatistical significanceStatistics education

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Area of Science:

  • Psychology
  • Statistics Education
  • Research Methodology

Background:

  • Traditional statistics education frequently emphasizes a 0.05 significance level for hypothesis testing.
  • This emphasis may lead to researchers developing mental representations where 0.05 acts as a boundary, influencing p-value interpretation.

Purpose of the Study:

  • To investigate the psychological impact of the 0.05 significance level on researchers' mental representations of probabilities.
  • To determine if the 0.05 threshold creates a discontinuity in the mental number line for p-values, altering reasoning.

Main Methods:

  • 25 graduate students with statistical training participated in the study.
  • Participants judged pairs of p-values as "similar" or "different."
  • Response times and judgments were analyzed, controlling for covariates.

Main Results:

  • Participants were significantly more likely and faster to classify p-values as "different" when they crossed the 0.05 boundary.
  • This effect suggests a categorical perception of p-values, similar to other psychological phenomena.
  • The 0.05 threshold appears to create a psychologically real divide between statistical significance and non-significance.

Conclusions:

  • Traditional statistical instruction may inadvertently foster a distorted perception of p-values.
  • This dichotomization of the p-value continuum is undesirable, especially with modern statistical approaches advocating for nuanced interpretation.
  • Rethinking statistical training is necessary to promote more accurate and less biased reasoning about statistical evidence.