Power of study can be increased by
First, the core concept here is statistical power in the context of a study. Power refers to the probability that a study will correctly reject the null hypothesis when the alternative hypothesis is true. It's the ability to detect an effect if one exists. Factors affecting power include sample size, effect size, significance level (alpha), and variability.
The question asks how to increase the power. Common methods are increasing sample size, increasing effect size, decreasing variability, or increasing the significance level. But in medical studies, increasing sample size is the most practical and commonly used method. So the correct answer is likely increasing sample size.
Now, for the wrong options, they might include things like decreasing sample size, which would reduce power, or lowering the significance level (e.g., from 0.05 to 0.01), which also decreases power. Another wrong option could be increasing variability, which would make it harder to detect an effect.
Clinical pearl: Remember that power is directly related to sample size. If you increase sample size, power increases, reducing the chance of a Type II error (beta). A high-yield fact for exams is that to increase study power, increasing sample size is the primary method.
Putting it all together, the correct answer is the option that mentions increasing sample size. The explanation needs to cover why sample size is the right answer and why others are wrong. Since the options weren't provided, I'll have to assume typical distractors. Let me structure the explanation accordingly.
**Core Concept**
Statistical power refers to a study's ability to detect a true effect or association when it exists. It is calculated as 1 - β (where β is the probability of a Type II error), and is influenced by sample size, effect size, variability, and significance level (α). Increasing power reduces the risk of missing a meaningful finding.
**Why the Correct Answer is Right**
The most effective way to increase a study's power is by **increasing the sample size**. A larger sample size reduces the standard error, enhancing the study's sensitivity to detect even small effect sizes. This is because the power of a test is directly proportional to the square root of the sample size, as per the formula for statistical power in hypothesis testing.
**Why Each Wrong Option is Incorrect**
**Option A:** Decreasing sample size reduces power by increasing the standard error, making it harder to detect true effects.
**Option B:** Lowering the significance level (e.g., α = 0.01 instead of 0.05) decreases power, as it raises the threshold for rejecting the null hypothesis.
**Option C:** Increasing variability (e.g., through poor study design) reduces power, as it obscures the signal-to-noise ratio.
**Clinical Pearl / High-Yield Fact**
Never forget: **"Bigger is better" for sample size in study design**. On exams, if asked how to increase power, prioritize sample size over other factors. Avoid confusing Type I (α) and Type II (β) errors—lowering α (e.g., to 0.01)