July 31, 2025QeiosOpen Access

Statistical and Substantive Significance of Pearson Bivariate Correlation Coefficients Under True or False Null Hypotheses

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A new teaching method for understanding statistical and substantive significance is presented.
Empirical computer simulations reveal how small sample sizes can misrepresent effect sizes.
The paper examines the role of sampling distributions of Pearson’s correlation coefficients in hypothesis testing.
Insights into true null hypothesis conditions shed light on common misunderstandings of statistical significance.

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Implication

This paper demonstrates the impact of sampling distributions on statistical significance in data analysis, highlighting issues with small sample sizes.

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Eugene Komaroff (2025) studied this question.

synapsesocial.com/papers/689a0c65e6551bb0af8cf8b3 https://doi.org/10.32388/ps72pk.3

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Implication

This paper demonstrates the impact of sampling distributions on statistical significance in data analysis, highlighting issues with small sample sizes.

Statistical and Substantive Significance of Pearson Bivariate Correlation Coefficients Under True or False Null Hypotheses

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Implication

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Statistical and Substantive Significance of Pearson Bivariate Correlation Coefficients Under True or False Null Hypotheses

Key Points

Discussion

Implication

Cite This Study

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