[Recommended] - Power analysis - ACADEMIC WRITERS BAY

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A research studys success begins with adequate statistical power. Power represents the study designs ability to identify visible relationships within the variables (Melnyk & Fineout-Overholt, 2019). The power of a study can be determined by performing a power analysis prior to completing the research. There are four components necessary to complete a power analysis and they include effect size, sample size, significance level, and power. Each of these components individually may not provide the necessary information to establish power, but together, an appropriate calculation can be completed providing the researcher with important data for the study.

Power is generally set at 0.80 or 80% and refers to the probability that the test will reject the null hypothesis (Brydges, 2019). Null hypothesis means there is no relationship among the different variables within the study. This power number represents that the actual findings will reject the null hypothesis correctly 80% of the time, and the other remaining 20% will report a false negative. A false negative result is called a type II error and occurs when there is an actual change in the effect, but the researcher concludes that there was no change (Kyriazos, 2018). The higher the power the lower the risk is in missing an actual effect with less chance of developing a type II error.

The use of effect size also helps determine an appropriate sample size for the study. Effect size represents the effect strength on the intervention with an inverse correlation between the sample size and effect size (Serdar et al., 2021). This means that with a smaller effect size, there needs to be a larger sample size to detect a smaller difference in the effect. This also works inversely with a larger effect size and a smaller sample size to detect the same effect difference. Effect size is represented by the symbol alpha and is commonly set at 0.05, meaning there is a 5% chance that the findings supporting the hypothesis will be false in the full population (Serdar et al., 2021). When appropriately calculated through power analysis, a researcher can feel confident that the study will adequately determine the effect difference while limiting the potential for type II errors.

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