AP Statistics 1.6: Describing Quantitative Distributions
Describe quantitative one-variable distributions using shape, center, spread, unusual features, and the context of the measured variable.
- Syllabus
- Effective Fall 2025
- Course
- AP Statistics
Describe quantitative one-variable distributions using shape, center, spread, unusual features, and the context of the measured variable.
A company sells a certain type of whistle. The price of the whistle varies from store to store. Julio, a statistician at the company, wants to estimate the mean price, in dollars ($), of this type of whistle at all stores that sell the whistle.
Julio wants to examine some characteristics of the distribution of the sample of whistle prices.
Julio called the managers of 20 randomly selected stores that sell the whistle and recorded the price of the whistle at each store. Following is a dotplot of Julio's data.
The summary statistics for Julio's data are shown in the following table.
| Sample Size | Mean | Standard Deviation | Minimum | Q1 | Median | Q3 | Maximum |
|---|---|---|---|---|---|---|---|
| 20 | 5.12 | 0.743 | 4.25 | 4.51 | 4.885 | 5.475 | 6.58 |
Summary Statistics for Julio's Data
Describe the shape of the distribution of the sample of whistle prices. Justify your response using appropriate values from the summary statistics table.
The distribution of the sample of whistle prices appears slightly skewed to the right, because the mean is slightly higher than the median.
It can often be difficult to determine whether the distribution of sample data is skewed by looking at a graph of the data and the summary statistics, particularly when the sample size is small. Thus, statisticians sometimes measure how skewed a data set is. One such measure is Pearson's coefficient of skewness, which is calculated using the following formula.
In the formula, xˉ is the sample mean, m is the sample median, and s is the sample standard deviation.
(i) Calculate Pearson's coefficient of skewness for Julio's sample of 20 whistle prices. Show your work.
The following graph shows conclusions that can be made about the shape of the distribution of sample data based on Pearson's coefficient of skewness and sample size.
Model Solution
0.7433(5.12−4.885)≈0.949.
Scoring components
1. Calculates Pearson’s coefficient of skewness with work shown.
Indicate the value of the Pearson's coefficient of skewness you calculated in part (c-i) for the appropriate sample size by marking it with an "X" on the preceding graph.

The point is marked at Pearson's coefficient of skewness approximately 0.949 and sample size 20.

Alternate accepted placement consistent with a rounded coefficient.
Scoring component
2. Places an X at approximately (0.949, 20), or consistently with the coefficient calculated in part (c-i).
Consider your work in part (c).
Julio's inference procedure in part (a-i) needs one of the following requirements to be satisfied to verify the normality condition.
- The sample size is greater than or equal to 30.
- If the sample size is less than 30, the distribution of the sample data is not strongly skewed and does not have outliers.
What should you conclude about the shape of the distribution of the sample of whistle prices? Justify your response.
Looking at the graph in part (c), for a sample size of 20, and a skewness coefficient of 0.949, this point falls in "the distribution of sample data is considered strongly skewed" region. Therefore, we would consider the shape of the distribution of the sample of whistle prices to be strongly skewed.