Podcast
Questions and Answers
What is one common measure of effect size for differences between means?
What is one common measure of effect size for differences between means?
- Statistical power
- Pearson's r
- Standard deviation
- Cohen's d (correct)
As sample size increases, the standard error of the mean decreases.
As sample size increases, the standard error of the mean decreases.
True (A)
What effect does increasing sample size have on statistical power?
What effect does increasing sample size have on statistical power?
It increases statistical power.
An effect size that achieves statistical significance may not always be ___ significant.
An effect size that achieves statistical significance may not always be ___ significant.
Match the following terms with their definitions:
Match the following terms with their definitions:
Which of the following reflects a potential misunderstanding when interpreting effect sizes?
Which of the following reflects a potential misunderstanding when interpreting effect sizes?
The effect size itself decreases as the sample size increases.
The effect size itself decreases as the sample size increases.
What is the main implication of larger sample sizes on interpreting effect sizes?
What is the main implication of larger sample sizes on interpreting effect sizes?
Flashcards
Effect Size
Effect Size
The magnitude of an observed effect or difference between groups. It quantifies the strength of the effect or the difference.
Statistical Power
Statistical Power
The probability of finding a statistically significant result if there is a real effect.
Standard Error
Standard Error
How much the sample mean (or other statistic) varies from the true population value. A smaller standard error indicates more precision in the estimate.
Sample Size and Statistical Power
Sample Size and Statistical Power
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Statistical Significance and Effect Size
Statistical Significance and Effect Size
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Standard Error and Sample Size
Standard Error and Sample Size
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Effect Size and Sample Size
Effect Size and Sample Size
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Practical vs. Statistical Significance
Practical vs. Statistical Significance
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Study Notes
Effect Size and Sample Size
- Effect size quantifies the magnitude of a phenomenon. Common measures include Cohen's d (mean differences) and Pearson's r (correlation).
Sample Size and Statistical Power
- Larger samples increase statistical power, enabling detection of smaller true effects.
Standard Error and Confidence Intervals
- Larger samples decrease the standard error, leading to narrower confidence intervals. This facilitates the detection of significant effects, even if they're small.
Effect Size Calculation and Context
- Effect size formulas (like Cohen's d) often include sample size, but the effect size itself doesn't decrease. Interpretation changes with sample size.
Statistical vs. Practical Significance
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Large samples can yield statistically significant results with very small effect sizes. These findings might not be practically significant.
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Researchers may report statistically significant results with low effect sizes, creating the illusion of a decreasing effect size.
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Increased sample size increases the probability of a statistically significant result, but doesn't necessarily mean the effect is meaningful.
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Description
This quiz explores the concepts of effect size and sample size in statistical analysis. Understand the relationship between sample size, statistical power, and the implications of effect size measures like Cohen's d and Pearson's r. Additionally, learn about the distinction between statistical and practical significance.
