Type I and II Errors/Tests

Front

What is a Type I error?

Back

A Type I error occurs when the null hypothesis is true, but we mistakenly reject it.

Front

What is a Type II error?

Back

A Type II error occurs when the null hypothesis is false, but we fail to reject it.

Front

What is the null hypothesis (H0)?

Back

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

Front

What is the alternative hypothesis (Ha)?

Back

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

Front

How can you reduce the likelihood of Type I and Type II errors?

Back

Increasing the sample size can help reduce both Type I and Type II errors.

Front

In hypothesis testing, what does it mean to reject the null hypothesis?

Back

Rejecting the null hypothesis means that there is sufficient evidence to support the alternative hypothesis.

Front

In hypothesis testing, 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 support the alternative hypothesis.

Front

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

Back

The significance level (alpha) is the probability of making a Type I error, commonly set at 0.05.

Front

What is a p-value in hypothesis testing?

Back

A p-value is the probability of obtaining test results at least as extreme as the observed results, assuming that the null hypothesis is true.

Front

What is the relationship between sample size and statistical power?

Back

As the sample size increases, the statistical power of a test also increases, reducing the likelihood of a Type II error.