Hypothesis testing Part 1

Front

What is a null hypothesis?

Back

The null hypothesis (H0) is a statement that there is no effect or no difference, and it serves as the default assumption in hypothesis testing.

Front

What is an alternative hypothesis?

Back

The alternative hypothesis (H1) is a statement that indicates the presence of an effect or a difference, opposing the null hypothesis.

Front

Can the null and alternative hypotheses be the same?

Back

No, the null and alternative hypotheses cannot be the same; they represent opposing statements.

Front

What does a right-tailed test indicate?

Back

A right-tailed test is used to determine if a parameter is greater than a certain value, indicated by H1: µ > k.

Front

What does a left-tailed test indicate?

Back

A left-tailed test is used to determine if a parameter is less than a certain value, indicated by H1: µ < k.

Front

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

Back

The significance level (alpha) is the probability of rejecting the null hypothesis when it is actually true, commonly set at 0.05.

Front

What is a Type I error?

Back

A Type I error occurs when the null hypothesis is rejected when it is true.

Front

What is a Type II error?

Back

A Type II error occurs when the null hypothesis is not rejected when it is false.

Front

What is the purpose of hypothesis testing?

Back

The purpose of hypothesis testing is to make inferences about population parameters based on sample data.

Front

What is a p-value?

Back

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