The alpha (α) level of a two-tailed hypothesis test using t will always be set at 0.05.

When constructing a confidence interval using the z distribution, as you increase the sample size, the width of the confidence interval will decrease.

The alpha (α) level of a one-tailed z hypothesis test is set at 0.05. The corresponding p-value calculated from your test statistic is 0.1367. Given the p-value, the most appropriate decision in this scenario would be to reject the null hypothesis of the test.

The mean of the sampling distribution (also known as the mean of all sample means) is equal to the population standard deviation.

Type I error occurs when you reject the null hypothesis when (unknown to us) it is, in fact, true.

Investigators want to test whether people who worked overnight shifts reported less sleep per day than the general population. They conduct a one-tailed, one-sample t-test, comparing the mean number of hours of sleep in a sample of people who worked overnight shifts (sample mean= 6.5 hours) to the general population mean of 7.5 hours. Their calculated t-statistic had a corresponding p-value of 0.0345. Which of the following represents the best interpretation of this p-value?

Which of the following is NOT true regarding the shape of normal curve:

A population of low birth weight infants is found to have a mean (µ) weight of 5 pounds with a standard deviation (σ) of 0.5 pounds. It is determined that approximately 80% of this population of infants falls between a z-score of +1.28 and –1.28. Another way of interpreting this information about this population of infants is:

Approximately what percentage of the area under a normal curve lies within 1 standard deviation of the mean?

When comparing a single sample to a population mean, a one-sample t-test must be used instead of a one-sample z-test when what value is unknown?