Understanding Hypothesis Testing

What is a null hypothesis?

• A null hypothesis is a statement that proves a significant effect exists.

• A null hypothesis is a statement that assumes no effect or no difference in a statistical test.

• A null hypothesis is a prediction of the outcome of an experiment.

• A null hypothesis is a claim that all data points are identical.

Define an alternative hypothesis.

• An alternative hypothesis is a conclusion drawn from the data without testing any assumptions.

• An alternative hypothesis is a statement that indicates the presence of an effect or difference.

• An alternative hypothesis is a statement that confirms the null hypothesis.

• An alternative hypothesis is a prediction that there will be no effect or difference.

What does the level of significance represent?

• The confidence level in a statistical test.

• The probability of making a Type II error in hypothesis testing.

• The threshold for determining statistical power.

• The probability of making a Type I error in hypothesis testing.

Explain the concept of a rejection region.

• A rejection region is the range of values that always leads to a conclusion.

• A rejection region is a statistical term that refers to the sample size used in testing.

• A rejection region is the range of values for which the null hypothesis is rejected in a hypothesis test.

• A rejection region is the area where the null hypothesis is accepted.

What are Type I and Type II errors in hypothesis testing?

• Type I error: accepting a false null hypothesis; Type II error: rejecting a true null hypothesis.

• Type I error: failing to accept a true null hypothesis; Type II error: accepting a false null hypothesis.

• Type I error: rejecting a true null hypothesis; Type II error: failing to reject a false null hypothesis.

• Type I error: failing to reject a true null hypothesis; Type II error: rejecting a false null hypothesis.

How do you identify the parameter to be tested in a real-life problem?

• Identify the key variable that influences the outcome of the problem.

• Test all variables simultaneously without focus.

• Select any variable regardless of its relevance.

• Choose a random variable without analysis.

Formulate a null hypothesis for a population mean.

• H0: μ ≠ 170

• H0: μ < 170

• H0: μ = 170

• H0: μ > 170

Formulate an alternative hypothesis for a population mean.

• H1: μ < 50

• H1: μ > 50

• H1: μ ≠ 50

• H1: μ = 50

What test statistic is used when the population variance is known?

• Chi-square test

• Z-test

• T-test

• ANOVA

What test statistic is used when the population variance is unknown?

• t-test

• ANOVA

• chi-square test

• z-test