Vista Academy • Data Analytics
Inferential Statistics
Learn how statisticians use sample data to understand populations, estimate unknown values, measure uncertainty, and make evidence-based statistical decisions.
What You Will Learn
Inferential statistics is about going beyond the data you directly observe. Instead of only describing a sample, you learn how to use sample evidence to estimate population characteristics and evaluate statistical claims.
Throughout this course, you will work with concepts such as sampling, sampling distributions, the Central Limit Theorem, confidence intervals, hypothesis testing, p-values, t-tests, chi-square tests, and ANOVA.
Sampling
Learn how samples are selected and why sampling matters when studying large populations.
Estimation
Learn how sample statistics can estimate unknown population parameters.
Confidence Intervals
Understand how statistical intervals communicate uncertainty in estimates.
Hypothesis Testing
Learn how statistical evidence is used to evaluate hypotheses.
Statistical Tests
Work with t-tests, chi-square tests, and ANOVA.
Data Analytics
Apply statistical concepts to realistic data analytics problems.
Inferential Statistics Course Curriculum
Complete all 20 lessons to move from the foundations of inferential statistics to practical statistical analysis.
Lesson 1: Introduction to Inferential Statistics
Start Lesson →Understand what inferential statistics is and how sample data can be used to learn about a population.
Lesson 2: Population vs Sample
Start Lesson →Learn the difference between populations, samples, parameters, and statistics.
Lesson 3: Sampling Methods
Start Lesson →Explore simple random, systematic, stratified, cluster, and convenience sampling.
Lesson 4: Sampling Distributions
Start Lesson →Understand how sample statistics behave across repeated samples.
Lesson 5: Central Limit Theorem
Start Lesson →Learn why the Central Limit Theorem is fundamental to statistical inference.
Lesson 6: Standard Error
Start Lesson →Understand standard error and how sample size affects estimation.
Lesson 7: Point Estimation
Start Lesson →Learn how sample statistics can be used as estimates of population parameters.
Lesson 8: Confidence Intervals
Start Lesson →Understand confidence intervals, uncertainty, and interval estimation.
Lesson 9: Confidence Interval for Mean
Start Lesson →Calculate and interpret confidence intervals for population means.
Lesson 10: Confidence Interval for Proportion
Start Lesson →Learn how to estimate population proportions using sample data.
Lesson 11: Hypothesis Testing
Start Lesson →Learn the basic logic and workflow behind statistical hypothesis testing.
Lesson 12: Null and Alternative Hypotheses
Start Lesson →Learn how to formulate null and alternative hypotheses correctly.
Lesson 13: p-value and Significance Level
Start Lesson →Understand p-values, significance levels, and statistical decisions.
Lesson 14: One-Sample t-Test
Start Lesson →Learn how to test a population mean using a one-sample t-test.
Lesson 15: Two-Sample t-Test
Start Lesson →Compare the means of two independent groups using sample data.
Lesson 16: Paired t-Test
Start Lesson →Learn how to analyze before-and-after or matched observations.
Lesson 17: Chi-Square Tests
Start Lesson →Understand chi-square tests for categorical data and relationships.
Lesson 18: ANOVA Basics
Start Lesson →Learn how ANOVA compares means across multiple groups.
Lesson 19: Type I, Type II Errors and Statistical Power
Start Lesson →Understand false positives, false negatives, and statistical power.
Lesson 20: Real-World Inferential Statistics Project
Start Lesson →Apply inferential statistics to a practical data analytics problem.
Learn Through Practice
This course is designed around understanding, calculation, practice, and application. Each interactive lesson builds on the concepts introduced earlier in the course.
Understand
Learn the statistical concept before applying it.
Calculate
Work through formulas and numerical examples.
Practice
Solve interactive activities and related questions.
Apply
Apply inferential statistics to practical analytics problems.
Your Learning Roadmap
LESSONS 1–6
Foundations
Population, samples, sampling, sampling distributions, CLT, and standard error.
LESSONS 7–10
Estimation
Point estimation and confidence intervals for means and proportions.
LESSONS 11–19
Statistical Testing
Hypothesis testing, p-values, t-tests, chi-square, ANOVA, errors, and statistical power.
LESSON 20
Real-World Project
Bring the full inferential statistics workflow together in a practical analytics problem.
Start Learning
Begin with Lesson 1
Start with the fundamentals and progress through all 20 lessons toward the final real-world inferential statistics project.