What is a priori power analysis, and why is it important?

• A. It estimates the required sample size to detect an expected effect with a chosen alpha; prevents underpowered studies
• B. It assesses data quality after data collection
• C. It determines the significance threshold to be used
• D. It is used to measure effect size after findings are known

Answer: (A)

Planning the study around power means deciding how many participants you need before collecting data so that, if the hypothesized effect exists, your test has a high chance of detecting it at the chosen significance level. An a priori power analysis uses the expected effect size, the alpha level (probability of a false positive), and the desired power (commonly 0.80 or 0.90) to compute the necessary sample size. Doing this beforehand helps ensure the study isn’t underpowered, reducing the risk of missing real effects (Type II error) and avoiding wasted resources from collecting too little data. It also guides feasibility and resource planning. Remember that it relies on reasonable guesses about the effect size; if those guesses are off, the final sample may still be too small or larger than needed. This planning is done before data collection, and it differs from analyses done after the data are in or after findings are known.