Will this help with my child's High School Statistics homework?

Yes. StudyPug covers every strand in the North Dakota High School Statistics course, including data distributions, probability, inference, and correlation, with worked examples and practice problems that match what students see in class.

How long are the video lessons?

Most video lessons run between 5 and 15 minutes, so students can watch a focused lesson on one concept - such as the normal distribution or conditional probability - without sitting through a full class period.

Is there a state test for High School Statistics in North Dakota?

North Dakota uses the NDSA for tested grades, but Statistics is typically an elective course rather than a required tested subject. Students should check with their school for any end-of-course requirements.

Can my child use StudyPug on a tablet or phone?

Yes. StudyPug works on tablets, phones, and computers, so your student can practice Statistics topics at home, at school, or on the go without needing to install any software.

Can I try StudyPug before paying?

Yes. StudyPug offers free sample lessons so you can see how the content works before subscribing. There is also a 30-day money-back guarantee if you decide it is not the right fit.

ND High School Statistics Curriculum

Give your student the practice they need to succeed in high school Statistics. Every topic follows the North Dakota Content Standards, from data analysis to probability and inference.

ND 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 North Dakota Statistics

North Dakota follows the ND Content Standards, organizing high school Statistics around interpreting data, drawing inferences, and understanding probability. This course covers data representation and distribution, comparing data sets, correlation and linear models, statistical inference, and probability - including conditional probability, random variables, and expected value. StudyPug provides video lessons, step-by-step practice problems, and instant feedback aligned to every strand in this course.

Strands: data representation, statistical inference, probability, and random variables
Video lessons for every Statistics topic in the ND Content Standards
Practice problems with step-by-step solutions for each strand
Aligned to the North Dakota High School Statistics curriculum

Curriculum topics by strand

Find lessons and practice for every strand, aligned to the North Dakota Content Standards for High School Statistics.

Topic includes:

Practice

Video

Quiz

Standard

StudyPug Topic

North Dakota High School Statistics Topics

Data Representation: Dot plots, histograms, and box plots on the real number line; scatter plots for two quantitative variables.
Comparing Data Sets: Center (mean, median) and spread (interquartile range, standard deviation); effects of outliers; normal distribution and population percentage estimates.
Categorical Data and Two-Way Tables: Two-way frequency tables, joint and marginal relative frequencies, associations, and trends.
Linear Models and Correlation: Correlation coefficient, interpreting linear fit, and distinguishing correlation from causation.
Statistical Inference: Sample surveys, experiments and observational studies; margin of error; randomized experiments and significance; evaluating data-based reports.
Probability: Independent events, conditional probability, Addition Rule, Multiplication Rule, and the use of permutations and combinations.
Random Variables and Expected Value: Probability distributions, expected value, decision analysis, and applying probability to real-world strategies.

How StudyPug Helps North Dakota High School Statistics Students

When a student gets stuck on a concept like the normal distribution or conditional probability, StudyPug provides a short video lesson followed by practice problems with instant feedback. Each step-by-step solution walks through the reasoning so students understand the process, not just the answer.

StudyPug covers every strand in the course - from data representation through probability and inference - so students can revisit any topic at their own pace, whether they are preparing for a test or working through nightly assignments.

Aligned to North Dakota High School Statistics Curriculum

Every lesson and practice problem in StudyPug's Statistics course is aligned to the ND Content Standards, matching the strands and learning expectations that North Dakota high school teachers follow. Students practice exactly what their course requires, which means time spent on StudyPug directly supports what happens in class.

Ready to put your High School Math skills to the test?

Jump into practice questions for every ND High School Statistics topic - with instant feedback and step-by-step video solutions when you get stuck.

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Written by the StudyPug Math Team

Last updated June 5, 2026

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