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M09.02.09 Statistical power
In statistics, statistical power refers to the probability that a test will detect an effect if there is one. It’s the ability to reject the null hypothesis when it is false, thus reducing the risk of a Type II error (false negative). High statistical power indicates a lower chance of missing a true effect in the data.
Key Concept: Power and Type II Error
- Power (1 – β): The probability of correctly rejecting a false null hypothesis.
- Type II Error (β): The probability of failing to reject the null hypothesis when it is false, i.e., not detecting an effect when there is one.
Factors Affecting Statistical Power
Increasing statistical power makes it easier to detect an actual effect within the data. Several factors influence statistical power:
| Factor | Description |
|---|---|
| Sample Size | Increasing the sample size reduces variability and improves the likelihood of detecting a true effect, which increases statistical power. |
| Effect Size | The larger the effect size, the easier it is to detect, leading to higher statistical power. |
| Significance Level (α) | Lowering the significance level reduces Type I errors but can also impact power. Often, a balance is maintained at 0.05. |
| Measurement Precision | Better measurement instruments or techniques improve accuracy, thereby increasing power. |
| Variability Reduction | Decreasing variability within the data by controlling extraneous variables also increases power. |
Understanding Power Through Hypothesis Testing
The relationship between reality and research outcomes in hypothesis testing can be illustrated with a truth table:
| Reality | Research Conclusion | Outcome |
|---|---|---|
| Drug Works | Reject Null Hypothesis | Power |
| Drug Works | Fail to Reject Null Hypothesis | Type II Error (β) |
| Drug Does Not Work | Reject Null Hypothesis | Type I Error (α) |
| Drug Does Not Work | Fail to Reject Null Hypothesis | Correct Decision |
Important Points to Remember
- Power and Sample Size: Increasing sample size is the most common and effective way to increase statistical power.
- Balance with Type I Error: While increasing power reduces Type II errors, it’s essential to consider the risk of Type I errors to avoid false positives.
- Influence of Effect Size: Large effect sizes are more easily detectable, increasing power without requiring large sample sizes.
Bibliography
- Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Routledge.
- Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. Wiley.
- Rosenthal, R. (1991). Meta-Analytic Procedures for Social Research. Sage Publications.