Will this help with my child's high school Statistics homework?

Yes. StudyPug covers every strand in Pennsylvania high school Statistics, from data distributions and scatter plots to probability rules and inference, so your child can get help on exactly what was assigned in class.

How long are the video lessons for high school Statistics?

Most video lessons run between a few minutes and around ten minutes, keeping them focused on one concept at a time. Students can rewatch any lesson as often as they need without waiting for the next class.

Is there a Pennsylvania state test for high school Statistics?

Pennsylvania's PSSA is administered in grades 3 through 8. High school math assessments use the Keystone Exams, which focus on Algebra I and other core courses rather than Statistics specifically.

Can my child use StudyPug on a tablet or phone?

Yes. StudyPug works on tablets, phones, and computers, so your child can practice Statistics problems at home, on the go, or wherever it is convenient for them.

Can I try StudyPug before paying?

Yes. StudyPug offers free sample lessons so you can see how the content works before committing. There is also a 30-day money-back guarantee if the subscription is not the right fit for your family.

PA High School Statistics Curriculum

Give your student the support they need to succeed in high school Statistics. Our lessons and practice problems follow what Pennsylvania schools actually teach, covering data analysis, probability, and statistical inference.

PA High School Statistics Curriculum

IDStandardStudyPug Topic

CC.HSS.ID.A.1Represent data with plots on the real number line (dot plots, histograms, and box plots).
- Why learn log?
- Equation of the best fit line
- The determinant of a 2 x 2 matrix
- Frequency distribution and histograms
CC.HSS.ID.A.2Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets.
- Median and mode
- Mean
- Range and outliers
- Center of a data set: mean, median, mode
- Spread of a data set - standard deviation & variance
CC.HSS.ID.A.3Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
- Application of averages
- Shapes of distributions
CC.HSS.ID.A.4Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve.
- Introduction to normal distribution
- Normal distribution and continuous random variable
- Z-scores and random continuous variables
CC.HSS.ID.B.5Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data.
- Reading and drawing Venn diagrams
- Probability with Venn diagrams
CC.HSS.ID.B.6Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
- Reading and drawing line graphs
- Bivariate, scatter plots and correlation
CC.HSS.IC.A.1Understand statistics as a process for making inferences about population parameters based on a random sample from that population.
- Sampling methods
CC.HSS.IC.A.2Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation.
- Sampling distributions
- Rare event rule
CC.HSS.IC.B.3Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each.
- Census and bias
CC.HSS.IC.B.4Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling.
- Margin of error
- Confidence intervals to estimate population mean
- Making a confidence interval
CC.HSS.IC.B.5Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.
- Traditional hypothesis testing
- P-value hypothesis testing
- Analysis of variance (ANOVA)
CC.HSS.IC.B.6Evaluate reports based on data.
- Influencing factors in data collection
CC.HSS.CP.A.2Understand that two events A and B are independent if the probability of A and B occurring together is the product of their probabilities, and use this characterization to determine if they are independent.
- Probability of independent events
- Multiplication rule for
- Probability of independent events
CC.HSS.CP.A.3Understand the conditional probability of A given B as P(A and B)/P(B), and interpret independence of A and B as saying that the conditional probability of A given B is the same as the probability of A, and the conditional probability of B given A is the same as the probability of B.
- Conditional probability
CC.HSS.CP.A.5Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations.
- Comparing experimental and theoretical probability
CC.HSS.CP.B.7Apply the Addition Rule, P(A or B) = P(A) + P(B) - P(A and B), and interpret the answer in terms of the model.
- Addition rule for
CC.HSS.CP.B.9Use permutations and combinations to compute probabilities of compound events and solve problems.
- Permutations
- Combinations
- Probability involving permutations and combinations
CC.HSS.MD.A.1Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space; graph the corresponding probability distribution using the same graphical displays as for data distributions.
- Probability distribution - histogram, mean, variance & standard deviation
CC.HSS.MD.A.2Calculate the expected value of a random variable; interpret it as the mean of the probability distribution.
- Properties of expectation
CC.HSS.MD.A.3Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated; find the expected value.
- Binomial distribution
- Mean and standard deviation of binomial distribution
CC.HSS.MD.B.7Analyze decisions and strategies using probability concepts (e.g., product testing, medical testing, pulling a hockey goalie at the end of a game).
- Type 1 and type 2 errors

Statistics

About High School Pennsylvania Statistics

Pennsylvania high school Statistics follows the Pennsylvania Core Standards, organizing content into strands covering data representation and distribution, bivariate data and scatter plots, statistical inference and sampling, probability and independence, and random variables and expected value. Students develop skills in interpreting data displays, applying probability rules, and drawing conclusions from experiments and surveys. StudyPug provides video lessons, step-by-step practice problems, and worked examples aligned to every strand in this course.

Strands covered: data representation, bivariate data, statistical inference, probability, and random variables
Video lessons for every Statistics topic taught in Pennsylvania high schools
Practice problems and worked examples with step-by-step explanations
Content aligned to Pennsylvania Core Standards for high school Statistics

Curriculum topics by strand

Find lessons and practice for every strand in Pennsylvania high school Statistics, aligned to Pennsylvania Core Standards.

Topic includes:

Practice

Video

Quiz

Standard

StudyPug Topic

Pennsylvania High School Statistics Topics

Data representation and distribution: Representing data with dot plots, histograms, and box plots; comparing center (mean, median) and spread (interquartile range, standard deviation); interpreting outliers; fitting data to a normal distribution and estimating population percentages.
Bivariate data and scatter plots: Summarizing categorical data in two-way frequency tables; representing two quantitative variables on scatter plots; computing and interpreting the correlation coefficient; distinguishing between correlation and causation.
Statistical inference and sampling: Making inferences about population parameters from random samples; understanding sample surveys, experiments, and observational studies; estimating population means and proportions with margins of error; evaluating reports based on data.
Probability and independence: Understanding independent events and conditional probability; constructing and interpreting two-way frequency tables as sample spaces; applying the Addition Rule and the general Multiplication Rule; using permutations and combinations for compound events.
Random variables and expected value: Defining random variables and graphing probability distributions; calculating expected value; developing theoretical and empirical probability distributions; analyzing decisions and strategies using probability concepts.

How StudyPug Helps Pennsylvania High School Statistics Students

When a student gets stuck on a concept like conditional probability or interpreting a scatter plot, StudyPug lets them go back to the exact lesson they need, work through step-by-step examples, and then try practice problems until the idea clicks. There is no need to wait for the next class or a tutoring appointment.

Each lesson is broken into manageable steps, so a student who is confused about the difference between an observational study and a randomized experiment, or how to apply the normal distribution, can isolate that one idea and build from there at their own pace.

Aligned to Pennsylvania High School Statistics Curriculum

Every lesson and practice problem on StudyPug is built around the Pennsylvania Core Standards for high school Statistics, so students are always working on the skills and concepts their class expects them to know - from data displays and probability rules through statistical inference and decision analysis.

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Written by the StudyPug Math Team

Last updated June 5, 2026

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