90% power

Definition

90% power refers to the probability that a statistical test will correctly reject the null hypothesis when it is false, indicating a high likelihood of detecting an effect if one truly exists. This concept is vital in experimental design, as it helps researchers determine the sample size needed to achieve reliable results and minimize the risk of Type II errors, which occur when a test fails to detect a true effect.

5 Must Know Facts For Your Next Test

1. Achieving 90% power typically requires larger sample sizes compared to lower power levels, which can lead to more accurate and trustworthy results.
2. Higher power reduces the risk of Type II errors, making it more likely that researchers will identify meaningful effects in their data.
3. Power analysis is often conducted before data collection to determine the necessary sample size for achieving 90% power.
4. The relationship between effect size and power indicates that larger effect sizes generally require smaller samples to achieve the same power level.
5. In practice, researchers often aim for at least 80% power; however, 90% power is preferred when the consequences of missing an effect are significant.

Review Questions

• How does achieving 90% power influence the design of an experiment?
  • Achieving 90% power significantly influences experimental design by guiding researchers in determining the appropriate sample size needed to confidently detect an effect. When researchers aim for this level of power, they need to consider various factors, including expected effect size and significance level. This careful planning helps ensure that the study has a strong chance of detecting real differences or relationships in the data, thereby enhancing its credibility and reliability.
• Discuss the implications of having low statistical power in a study and how it relates to Type II errors.
  • Low statistical power can have serious implications for a study's findings, as it increases the likelihood of Type II errors, where a true effect goes undetected. When a study lacks sufficient power, it may fail to identify meaningful differences or relationships, leading researchers to conclude erroneously that no effect exists. This can be particularly problematic in fields where detecting subtle effects is crucial, as it may hinder advancements in understanding or treatment options based on misleading findings.
• Evaluate the importance of conducting power analysis prior to data collection and its impact on research outcomes.
  • Conducting power analysis prior to data collection is essential for ensuring that research outcomes are valid and meaningful. By estimating the necessary sample size needed to achieve 90% power based on expected effect sizes and significance levels, researchers can avoid wasting resources on studies that lack sufficient strength. This proactive approach not only minimizes Type II errors but also enhances the overall credibility of research findings, ultimately contributing to more robust and reliable scientific knowledge.

Related terms

Type I Error:

The incorrect rejection of a true null hypothesis, also known as a false positive, where the test suggests an effect exists when it does not.

Type II Error:

The failure to reject a false null hypothesis, also known as a false negative, where the test fails to detect an effect that is actually present.

Effect Size:

A quantitative measure of the magnitude of the difference or relationship observed in a study, which aids in understanding the practical significance of research findings.