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Quizzes > Mathematics

AP Stats Chapter 4 Quiz: 20 Practice Questions

Quick practice for your AP Statistics Chapter 4 test. Instant results.

Editorial: Review CompletedCreated By: Anas AmmaUpdated Aug 25, 2025
Difficulty: Moderate
Grade: Grade 11
Study OutcomesCheat Sheet
Paper art illustrating a trivia quiz for high school students on Ch. 4 Stats Showdown

This AP Stats Chapter 4 quiz helps you check your understanding of key ideas and methods before your next test. Tackle 20 AP-style questions, see your score, and spot topics to review. For more practice, try the AP Stats unit 1 quiz, build confidence with normal distribution practice, or explore broader skills with statistics sample questions.

Which measure of center is most resistant to outliers?
Median
Mean
Range
Standard Deviation
The median, being the middle value, is less affected by extreme values than the mean. Other measures like the mean, range, and standard deviation are more sensitive to outliers.
What type of plot displays the distribution of a dataset by splitting each data value into a stem and a leaf?
Stem-and-Leaf Plot
Histogram
Box Plot
Scatter Plot
A stem-and-leaf plot organizes numerical data where the stem represents the leading digit(s) and the leaf represents the trailing digit. This display helps in visualizing the shape and distribution of the data.
Which measure of spread is calculated as the difference between the largest and smallest values in a dataset?
Interquartile Range
Variance
Range
Standard Deviation
The range is defined as the difference between the maximum and minimum values in the dataset. It provides a quick measure of the total spread but can be heavily influenced by outliers.
If a distribution is symmetric, what can be said about its mean and median?
They are approximately equal.
The mean is greater than the median.
They cannot be compared.
The mean is less than the median.
In a symmetric distribution, the mean and median coincide because the distribution is balanced around the center. This equality is a key property used to identify symmetric distributions.
Which graphical representation is best for displaying the five-number summary of a dataset?
Histogram
Bar Graph
Box Plot
Pie Chart
A box plot is specifically designed to display the five-number summary, which includes the minimum, first quartile, median, third quartile, and maximum. It provides a clear visual representation of the data's spread and central tendency.
In a box plot, what do the whiskers typically represent?
The central half of the data
The first and third quartiles
The smallest and largest observations that are not considered outliers
The raw minimum and maximum values in the data
In a box plot, the whiskers extend to the most extreme data points that are not outliers, usually defined as those within 1.5 times the interquartile range from the quartiles. This method helps to clearly differentiate typical values from outliers.
What is the interquartile range (IQR) a measure of?
Central tendency
Variability in the entire dataset
The spread of the middle 50% of the data
The total range of the dataset
The IQR is calculated as the difference between the third quartile (Q3) and the first quartile (Q1), representing the spread of the central 50% of the data. It is a robust measure of variability that is not influenced by outliers.
How does adding a constant to every data point in a distribution affect the standard deviation?
It increases the standard deviation by the constant.
It decreases the standard deviation by the constant.
It does not change the standard deviation.
It doubles the standard deviation.
Adding a constant shifts the entire distribution without affecting its spread. Since standard deviation measures dispersion around the mean, it remains unchanged when the data are uniformly shifted.
If every score in a dataset is multiplied by a constant, what happens to the variance?
It remains the same.
It is divided by the constant.
It is multiplied by the square of the constant.
It is multiplied by the constant.
When data are scaled by a constant factor, the variance is affected by the square of that factor because variance involves squared deviations from the mean. This property is key in understanding how scaling impacts data dispersion.
Which of the following is a characteristic of a normal distribution?
The mean is always greater than the median.
The variance is undefined.
It has skewed tails.
It is symmetric about its mean.
A key property of a normal distribution is its symmetry about the mean, meaning the left and right sides of the distribution are mirror images. This symmetry is foundational for many statistical analyses.
Given a dataset with a few unusually high values, which measure of center is most appropriate?
Range
Mode
Mean
Median
When a dataset contains outliers, the median provides a better measure of central tendency than the mean because it is not influenced by extreme values. The median accurately reflects the center of the majority of the data.
What does a z-score represent in a dataset?
The number of standard deviations a data point is from the mean.
The value of a data point after normalization.
The percentile rank of a data point.
The absolute difference between a data point and the median.
A z-score standardizes a data point by indicating how many standard deviations it is away from the mean. This standardization facilitates comparisons across different datasets or distributions.
In a histogram, if the bars are taller on the left side and gradually decrease in height to the right, the distribution is likely:
Negatively skewed.
Symmetric.
Positively skewed.
Uniform.
A histogram with taller bars on the left indicates that the bulk of the data is concentrated on the lower end, with a longer tail to the right. This pattern is characteristic of a positively skewed distribution.
Which term best describes the middle value in an ordered dataset?
Mean
Range
Median
Mode
The median is the value that divides an ordered dataset into two equal halves. It is an essential measure of central tendency, particularly useful in skewed distributions.
What is the primary purpose of using a scatterplot in statistics?
To show how a single variable is distributed across categories.
To examine the relationship between two quantitative variables.
To represent parts of a whole.
To display the frequency distribution of a single variable.
Scatterplots are used to visualize the relationship between two numerical variables, helping to reveal potential correlations or patterns. This graphical tool is fundamental in bivariate data analysis.
When comparing two datasets, which graphical representation allows for the most comprehensive comparison of both spread and central tendency?
Box-and-Whisker Plot
Pie Chart
Histogram
Stem-and-Leaf Plot
A box-and-whisker plot provides a five-number summary, displaying the median, quartiles, and potential outliers. This makes it an excellent tool for comparing the central tendency and variability across different datasets.
If a dataset is heavily skewed with outliers, which transformation is most likely to reduce the skewness?
Adding a constant to each data point
Squaring each data point
Multiplying each data point by a constant
Logarithmic transformation
A logarithmic transformation compresses the scale of large values more than small ones, which can help reduce positive skewness. This method is commonly used to normalize data distributions with extreme values.
A researcher collects data on student test scores and notices a high degree of variability. Which statistic best describes this variability and why?
Median, because it is not influenced by extreme values.
Mean, because it represents the center of the data.
Mode, because it identifies the most frequently occurring score.
Standard Deviation, because it quantifies the average distance from the mean.
Standard deviation is a measure that indicates how spread out the data points are from the mean. It provides a clear picture of the variability within a dataset, which is essential when comparing the dispersion of scores.
In a box plot, the whiskers extend to the most extreme data points that are not considered outliers. How are these outliers typically defined?
Data points that occur with very low frequency.
Data points that are more than 1.5 times the IQR from the quartiles.
The smallest and largest values in the dataset.
Data points falling outside the range of the mean ± 2 standard deviations.
Outliers in a box plot are typically defined as data points that lie more than 1.5 times the interquartile range (IQR) beyond the first or third quartile. This rule of thumb helps in identifying and excluding atypical values when summarizing the data.
When sampling from a population, which condition is necessary for the Central Limit Theorem to apply effectively?
A sufficiently large sample size, typically n ≥ 30.
The data must be measured on a nominal scale.
The population must be normally distributed.
There must be no outliers in the dataset.
The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, generally accepted as n ≥ 30. This holds true regardless of the shape of the original population distribution, provided the sample is random and independent.
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Study Outcomes

  1. Analyze data distributions and summarize key statistical measures.
  2. Interpret graphical representations such as histograms and box plots.
  3. Apply probability concepts to solve practical problems.
  4. Evaluate the effectiveness of statistical models through hypothesis testing.
  5. Determine the implications of sampling methods on data accuracy.

AP Stats Ch 4 Review Cheat Sheet

  1. Law of Large Numbers - As you crank up the number of trials in a random experiment, the observed proportion of a specific outcome zeroes in on its true probability. Imagine rolling a die endlessly until the fraction of sixes settles around one‑sixth. This principle is your go‑to tool for predicting long‑term behavior in probability.
  2. Addition Rule for Probabilities - Whenever you want the chance of either event A or B happening, add P(A) and P(B), then subtract the overlap P(A and B) to avoid double‑counting. It's like making sure you don't count the same piece of pizza twice when you're hungry! Mastering this rule keeps your probability calculations neat and accurate.
  3. Multiplication Rule for Independent Events - If two events don't affect each other, the chance both occur is just P(A) × P(B). Think of flipping a coin and rolling a die - getting heads and a four is the product of each chance. This rule is essential whenever you're stacking independent probabilities.
  4. Conditional Probability - To find the probability of A given B has occurred, use P(A|B) = P(A and B)/P(B). It's like asking, "What's the chance I ace this test now that I've already aced the practice quiz?" This formula unlocks the world of dependent events.
  5. Discrete vs. Continuous Variables - Discrete variables have countable outcomes (like the number of heads in ten coin flips), while continuous variables can take any value in a range (like your height). Knowing the difference guides you in choosing the right probability model. This distinction helps you decide whether to use sums or integrals!
  6. Binomial Distributions - Perfect for a fixed number of identical, independent trials with "success" or "failure" outcomes. The probability of exactly x successes in n trials is P(X=x) = (n choose x) p^x (1 - p)^(n - x). Use this when you're counting wins, heads, or any yes/no scenario.
  7. Geometric Distributions - This one tracks how many trials it takes to get the first success in repeated independent Bernoulli trials. The chance the first win comes on trial x is P(X=x) = (1 - p)^(x - 1) p. It's your go‑to for "how many tries until we nail it?" questions.
  8. Expected Value - The expected value (or mean) of a random variable is E(X) = Σ[x * P(x)], giving you the "center of mass" for its distribution. It tells you what to expect on average if you could repeat the experiment infinitely. Think of it as the long-run average of your bets or gains.
  9. Variance & Standard Deviation - Variance measures the average squared distance from the mean, and standard deviation is its square root. These stats show you how "spread out" your outcomes are around the expected value. They're essential for gauging risk and reliability in data.
  10. Simulations for Estimating Probabilities - When theory gets tough, let computers run thousands of trials and watch the empirical probabilities emerge. Simulations are like virtual labs for probability, helping you explore scenarios that are hard to crunch by hand. They're perfect for building intuition and checking your formulas.
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