Basic Facts About Hypothesis Testing

A ​ (a)   test is a formal procedure for using observed ​ (b)   to decide between two different claims, called ​ (c)   . These statements are about a​ (d)   , population proportion or population ​ (e)   .

• significance
• data
• hypotheses
• parameter
• mean
• conjectures
• statistic
• mode
• questions

What is the null hypothesis in hypothesis testing?

• A statement that there is a significant effect or difference.

• A statement that there is no effect or difference.

• A statement that the sample size is too small.

• A statement that the data is normally distributed.

What is the alternative hypothesis in hypothesis testing?

• A statement that there is no effect or difference.

• A statement that the sample size is too large.

• A statement that there is a significant effect or difference.

• A statement that the data is skewed.

How is the p-value calculated in hypothesis testing?

• By dividing the sample mean by the population mean.

• By comparing the test statistic to a standard normal distribution.

• By multiplying the sample size by the standard deviation.

• By adding the significance level to the test statistic.

What does a significance level of 0.05 mean in hypothesis testing?

• There is a 5% chance of accepting the null hypothesis when it is true.

• There is a 5% chance of rejecting the null hypothesis when it is true.

• There is a 95% chance of rejecting the null hypothesis when it is false.

• There is a 95% chance of accepting the null hypothesis when it is false.

If the p-value is less than the significance level, what decision should be made regarding the null hypothesis?

• Accept the null hypothesis.

• Fail to reject the null hypothesis.

• Reject the null hypothesis.

• Increase the sample size.

If the p-value is greater than the significance level, what decision should be made regarding the null hypothesis?

• Accept the null hypothesis.

• Fail to reject the null hypothesis.

• Reject the null hypothesis.

• Decrease the sample size.

Which of the following best describes a p-value?

• The probability of observing the data given that the null hypothesis is true.

• The probability of the null hypothesis being true.

• The probability of the alternative hypothesis being true.

• The probability of the data being normally distributed.

What is the typical threshold for a significance level in many scientific studies?

• 0.01

• 0.05

• 0.10

• 0.50

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

• The null hypothesis is proven true.

• There is not enough evidence to support the alternative hypothesis.

• The alternative hypothesis is proven true.

• The data is not normally distributed.