Evaluating P-Values

Front

What is a P-value?

Back

A P-value indicates the probability of obtaining the observed data, or something more extreme, assuming that the null hypothesis is true.

Front

What does a small P-value (e.g., < 0.05) suggest about the null hypothesis?

Back

A small P-value suggests strong evidence against the null hypothesis, leading to its rejection.

Front

What is the null hypothesis in hypothesis testing?

Back

The null hypothesis is a statement that there is no effect or no difference, and it serves as the default assumption to be tested.

Front

What is the alternative hypothesis?

Back

The alternative hypothesis is a statement that indicates the presence of an effect or a difference, opposing the null hypothesis.

Front

What does it mean to reject the null hypothesis?

Back

Rejecting the null hypothesis means that the evidence suggests that the null hypothesis is unlikely to be true.

Front

What does it mean to fail to reject the null hypothesis?

Back

Failing to reject the null hypothesis means that there is not enough evidence to conclude that the null hypothesis is false.

Front

What is the significance level (alpha) in hypothesis testing?

Back

The significance level (alpha) is the threshold for determining whether to reject the null hypothesis, commonly set at 0.05.

Front

What is a Type I error?

Back

A Type I error occurs when the null hypothesis is true, but is incorrectly rejected.

Front

What is a Type II error?

Back

A Type II error occurs when the null hypothesis is false, but is incorrectly failed to be rejected.

Front

How does sample size affect P-values?

Back

Larger sample sizes tend to produce more reliable estimates and can lead to smaller P-values, making it easier to detect true effects.