Imagine six fictitious example studies that each examine whether a new app called StatMaster can help students learn statistical concepts better than traditional methods. We encourage the use of this chart in helping your students understand and interpret results as they study various research studies or methodologies. Rather than concentrate on only the p-value result, which has so often traditionally been the focus, this chart (and the examples below) help students understand how to look at power, sample size, and effect size in conjunction with p-value when analyzing results of a study. This tool can help a student critically analyze whether the research study or article they are reading and interpreting has acceptable power and sample size to minimize error. This concept is important for teachers to develop in their own understanding of statistics, as well. Therefore, the chart in Figure 1 is a tool that can be useful when introducing the concept of power to an audience learning statistics or needing to further its understanding of research methodology.įigure 1 A tool that can be useful when introducing the concept of power to an audience learning statistics or needing to further its understanding of research methodology We have found students generally understand the concepts of sampling, study design, and basic statistical tests, but sometimes struggle with the importance of power and necessary sample size. Having stated a little bit about the concept of power, the authors have found it is most important for students to understand the importance of power as related to sample size when analyzing a study or research article versus actually calculating power. In terms of significance level and power, Weiss says this means we want a small significance level (close to 0) and a large power (close to 1). In reality, a researcher wants both Type I and Type II errors to be small. There are other variables that also influence power, including variance ( σ2), but we’ll limit our conversation to the relationships among power, sample size, effect size, and alpha for this discussion. Power is increased when a researcher increases sample size, as well as when a researcher increases effect sizes and significance levels. Magnitude of the effect of the variable.Variability, or variance, in the measured response variable.Powers lower than 0.8, while not impossible, would typically be considered too low for most areas of research.īullard also states there are the following four primary factors affecting power: Beta is commonly set at 0.2, but may be set by the researchers to be smaller.Ĭonsequently, power may be as low as 0.8, but may be higher. The power of a hypothesis test is between 0 and 1 if the power is close to 1, the hypothesis test is very good at detecting a false null hypothesis. Simply put, power is the probability of not making a Type II error, according to Neil Weiss in Introductory Statistics.
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