Final answer:
To determine if the population distribution from which the sample was selected is normal, we can examine the shape of the sample data and perform a normality test.
Step-by-step explanation:
To determine if the population distribution from which the sample was selected is normal, we can examine the shape of the sample data. One way to do this is by creating a histogram or a boxplot. However, with a small sample size of 13, it may be difficult to draw definitive conclusions about the population distribution. Additionally, we can perform a normality test, such as the Shapiro-Wilk test, to assess the normality assumption.
The Shapiro-Wilk test compares the observed sample data to what would be expected from a normal distribution. If the p-value of the test is greater than the significance level (typically 0.05), we do not have enough evidence to reject the null hypothesis that the data follow a normal distribution. Conversely, if the p-value is less than the significance level, we have evidence to reject the null hypothesis and conclude that the data do not follow a normal distribution.
To perform the Shapiro-Wilk test, the data should be inputted into statistical software or calculators that offer this test. The output will provide the p-value, which can be compared to the significance level to draw a conclusion about the normality assumption.