**Core Concept**
The significance level, denoted by 'p', is a crucial statistical parameter used in hypothesis testing to determine the probability of obtaining a result as extreme or more extreme than the one observed, assuming that the null hypothesis is true. It represents the maximum probability of rejecting the null hypothesis when it is actually true.

**Why the Correct Answer is Right**
The correct answer, D. 0.05, is the traditional significance level commonly used in medical research and hypothesis testing. This value is chosen because it represents a balance between the risk of Type I error (rejecting a true null hypothesis) and the risk of Type II error (failing to reject a false null hypothesis). A significance level of 0.05 means that there is a 5% chance of obtaining the observed result or a more extreme result by chance, assuming that the null hypothesis is true. This threshold allows researchers to make a decision about the null hypothesis based on the p-value.

**Why Each Wrong Option is Incorrect**
**Option A:** A significance level of 0.01 is too stringent and is often used in more conservative research settings. While it reduces the risk of Type I error, it may also increase the risk of Type II error.

**Option B:** A significance level of 0.02 is not a commonly used threshold in medical research and may lead to inconsistent results.

**Option C:** A significance level of 0.04 is also not a standard threshold and may not provide a clear interpretation of the results.

**Clinical Pearl / High-Yield Fact**
When interpreting p-values, it's essential to remember that a p-value of 0.05 does not necessarily mean that a result is true or significant; it only means that the observed result is unlikely to occur by chance, assuming that the null hypothesis is true. Additionally, p-values should be interpreted in context and in conjunction with other study results.

**✓ Correct Answer: D. 0.05**