Statistics — Chapters
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Statistics on STEM Calculators is a collection of interactive statistics calculators built for learning and applying statistical methods in school, university, and real-world data analysis. It helps you compute results quickly while reinforcing correct reasoning with clear outputs that support understanding, not just answers.
This subject typically includes tools for descriptive statistics (mean, median, standard deviation, variance, percentiles), probability and random variables, and common probability distributions such as normal, binomial, Poisson, t, chi-square, and F distributions. Many calculators also cover z-scores, p-values, critical values, cumulative probabilities, and tail areas used in standard statistics coursework.
You’ll also find calculators for statistical inference, including confidence intervals, hypothesis testing (one-sample and two-sample tests, proportions, paired data), power and sample size planning, and comparisons across groups such as ANOVA at an introductory level. For applied statistics, tools often include correlation, simple linear regression, prediction, and model interpretation basics.
Difficulty ranges from beginner-friendly introductory stats (summary measures and probability) to more advanced topics used in university statistics and research methods (multi-step tests, distribution-based decisions, and regression/ANOVA outputs). This makes the page useful for high school statistics, AP/IB courses, first-year university classes, and anyone doing data-driven projects.
Students can verify homework and learn how to interpret results; teachers can generate examples and check solutions; self-learners can study with guided calculations; and advanced users can streamline repetitive computations for labs, research, and reporting. On this page you can input data or parameters, compute key statistics, and understand what the results mean—so you can make confident, evidence-based conclusions from data.
2. Numerical Descriptive Measures
3. Probability
4. Discrete Random Variables and Their Probability Distributions
- 1. Probability Distribution of a Discrete Random Variable
- 2. Mean of Discrete Random Variable
- 3. Standard Deviation of a Discrete Random Variable
- 4. Factorials Combinations and Permutations
- 5. The Binomial Probability Distribution
- 6. Using the Table of Binomial Probabilities
- 7. Probability of Success and the Shape of Binomial Distribution
- 8. Mean and Standard Deviation of Binomial Distribution
- 9. The Hypergeometric Probability Distribution
- 10. The Poisson Probability Distribution
- 11. Using the Table of Poisson Probabilities
5. Continuous Random Variables and the Normal Distribution
6. Sampling Distributions
7. Estimation of the Mean and Proportion
- 1. Point and Interval Estimates
- 2. Estimation of a Population Mean σ Known
- 3. Estimation of a Population Mean σ Not Known the T Distribution
- 4. Confidence Interval for Mean Using the T Distribution
- 5. Estimation of a Population Proportion Large Samples
- 6. Determining the Sample Size for the Estimation of Proportion
8. Hypothesis Tests About the Mean and Proportion
- 1. Hypothesis Tests
- 2. Hypothesis Tests About μ, σ Known, the P Value Approach
- 3. Hypothesis Tests About μ, σ Known, the Critical Value Approach
- 4. Hypothesis Tests About μ, σ Not Known, the P Value Approach
- 5. Hypothesis Tests About μ, σ Not Known, the Critical Value Approach
- 6. Hypothesis Tests About a Population Proportion Large Samples the P Value Approach
- 7. Hypothesis Tests About a Population Proportion Large Samples the Critical Value Approach
9. Estimation and Hypothesis Testing, Two Populations
- 1. Inferences About the Difference Between Two Population Means for Independent Samples, σ₁ and σ₂ Known
- 2. Two Population Means for Independent Samples, σ₁ and σ₂ Unknown but Equal
- 3. Two Population Means for Independent Samples, σ₁ and σ₂ Unknown and Unequal
- 4. Inferences About the Difference Between Two Population Means for Paired Samples
- 5. Inferences About the Difference Between Two Population Proportions for Large and Independent Samples