Power

Study power is a crucial concept in research, determining how well a study can detect a true effect when one exists.

To illustrate this, imagine Superman—his heightened abilities allow him to detect crimes before others. Similarly, a powerful study can identify meaningful effects while avoiding false conclusions.

By definition, study power is the probability of correctly rejecting the null hypothesis when it is false. This means that a well-powered study ensures researchers do not miss real effects.

However, errors can still occur in hypothesis testing:

• Type 1 Error (False Positive): Rejecting the null hypothesis when it is true, leading to incorrect conclusions (e.g., assuming a treatment works when it doesn’t).

• Type 2 Error (False Negative): Failing to reject the null hypothesis even when it is false, causing researchers to overlook effective treatments.

The significance level (α) represents the probability of making a Type 1 error, while power helps avoid Type 2 errors.

Several factors influence study power:

1. Sample Size – Larger samples improve the ability to detect effects, increasing power.

2. Effect Size – Bigger effects are easier to detect, whereas smaller effects require larger samples to achieve the same power.

3. Significance Level (α) – Raising α can increase power but also raises the risk of Type 1 errors.

Power is essential because a study without sufficient power might miss important findings, wasting time and resources.

By optimising sample size, effect size, and significance level, researchers can ensure their studies are well-powered to detect meaningful differences—just like Superman spotting hidden dangers.