Y2 W7- Quizizz Weekly 10, Due 10/8 @ Midnight

What is the purpose of hypothesis testing in statistics?

• To make inferences or conclusions about a population based on sample data.

• To calculate the mean and standard deviation of a sample.

• To determine the sample size needed for a study.

• To summarize and describe the data collected in a study.

What is the null hypothesis in a one-sample t-test?

• The mean of the population is equal to the hypothesized value.

• There is a significant difference between the mean of the population and a hypothesized value.

• The null hypothesis is not applicable in a one-sample t-test.

• There is no significant difference between the mean of the population and a hypothesized value.

What is the alternative hypothesis in a one-sample t-test?

• The alternative hypothesis in a one-sample t-test is a statement that is only applicable in certain cases.

• The alternative hypothesis in a one-sample t-test is a statement that contradicts or negates the null hypothesis.

• The alternative hypothesis in a one-sample t-test is a statement that is unrelated to the null hypothesis.

• The alternative hypothesis in a one-sample t-test is a statement that supports the null hypothesis.

What is the formula for calculating the t-statistic in a one-sample t-test?

• (sample mean - population mean) / (sample standard deviation / sqrt(sample size))

• (sample mean + population mean) / (sample standard deviation / sqrt(sample size))

• (sample mean - population mean) / (sample standard deviation * sqrt(sample size))

• (sample mean - population mean) * (sample standard deviation / sqrt(sample size))

What is the degrees of freedom in a one-sample t-test?

• sample size minus 1

• sample size plus 1

• sample size divided by 2

• sample size multiplied by 2

What is the critical value for a two-tailed test with a significance level of 0.05?

• 1.96

• 1.64

• 2.33

• 0.05

What is the p-value in hypothesis testing?

• The p-value is the probability of obtaining a test statistic as extreme as, or more extreme than, the observed data, assuming that the null hypothesis is true.

• The p-value is the probability of obtaining a test statistic as extreme as, or less extreme than, the observed data, regardless of the hypothesis.

• The p-value is the probability of obtaining a test statistic as extreme as, or more extreme than, the observed data, assuming that the alternative hypothesis is true.

• The p-value is the probability of obtaining a test statistic as extreme as, or less extreme than, the observed data, assuming that the null hypothesis is true.

What is the decision rule for rejecting the null hypothesis in a one-sample t-test?

• If the calculated t-value is less than the critical t-value and the p-value is greater than the significance level, then we reject the null hypothesis.

• If the calculated t-value is greater than the critical t-value or if the p-value is less than the significance level, then we reject the null hypothesis.

• If the calculated t-value is less than the critical t-value or if the p-value is greater than the significance level, then we reject the null hypothesis.

• If the calculated t-value is equal to the critical t-value or if the p-value is equal to the significance level, then we reject the null hypothesis.

What is the interpretation of a p-value less than the significance level?

• The observed data is statistically significant and the null hypothesis is accepted.

• The observed data is statistically significant and the null hypothesis is rejected.

• The observed data is not statistically significant and the null hypothesis is rejected.

• The observed data is not statistically significant and the null hypothesis is accepted.

What are the assumptions for conducting a one-sample t-test?

• 1) The data is not normally distributed. 2) The observations are not independent of each other. 3) The dependent variable is measured on a discrete scale. 4) The sample is not randomly selected from the population. 5) The population standard deviation is known and is estimated from the sample.

• 1) The data is normally distributed. 2) The observations are dependent on each other. 3) The dependent variable is measured on a categorical scale. 4) The sample is not randomly selected from the population. 5) The population standard deviation is known and is not estimated from the sample.

• The assumptions for conducting a one-sample t-test are: 1) The data is normally distributed. 2) The observations are independent of each other. 3) The dependent variable is measured on a continuous scale. 4) The sample is randomly selected from the population. 5) The population standard deviation is unknown and is estimated from the sample.

• 1) The data is normally distributed. 2) The observations are independent of each other. 3) The dependent variable is measured on a categorical scale. 4) The sample is not randomly selected from the population. 5) The population standard deviation is unknown and is not estimated from the sample.