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Hypothesis Testing Courses

Hypothesis testing courses can help you learn statistical significance, p-values, confidence intervals, and the formulation of null and alternative hypotheses. You can build skills in analyzing data sets, interpreting results, and making informed decisions based on statistical evidence. Many courses introduce tools like R, Python, and Excel, which are commonly used for conducting tests such as t-tests, chi-square tests, and ANOVA, allowing you to apply your knowledge to real data analysis tasks.


Popular Hypothesis Testing Courses and Certifications


  • Status: Free Trial
    Free Trial
    G

    Google

    The Power of Statistics

    Skills you'll gain: Sampling (Statistics), Descriptive Statistics, Statistical Hypothesis Testing, Data Analysis, Probability Distribution, Statistics, Data Science, Statistical Analysis, A/B Testing, Statistical Methods, Probability, Statistical Inference, Statistical Programming, Python Programming, Technical Communication

    4.8
    Rating, 4.8 out of 5 stars
    ·
    866 reviews

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    M

    Meta

    Statistics Foundations

    Skills you'll gain: Bayesian Statistics, Descriptive Statistics, Statistical Hypothesis Testing, Statistical Inference, Sampling (Statistics), Data Modeling, Statistics, Probability & Statistics, Statistical Analysis, Statistical Methods, Statistical Modeling, Marketing Analytics, Tableau Software, Data Analysis, Spreadsheet Software, Analytics, Time Series Analysis and Forecasting, Regression Analysis

    4.8
    Rating, 4.8 out of 5 stars
    ·
    382 reviews

    Beginner · Course · 1 - 3 Months

  • Status: New
    New
    A

    Arizona State University

    Probability, Statistical Inference and Regression Analysis

    Skills you'll gain: Probability & Statistics, Analytical Skills, Exploratory Data Analysis, Estimation, Logistic Regression

    Intermediate · Course · 1 - 3 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    C

    Coursera

    Automate, Analyze, and Evaluate ML Experiments

    Skills you'll gain: MLOps (Machine Learning Operations), Model Evaluation, Key Performance Indicators (KPIs), Business Metrics, Performance Analysis, Performance Measurement, Responsible AI, Test Execution Engine, Performance Metric, Test Automation, Feature Engineering, Verification And Validation, Content Performance Analysis, Machine Learning, Data Ethics, Quality Assessment, Gap Analysis, Cost Benefit Analysis, Research Design, Quantitative Research

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    U

    University of Colorado Boulder

    Statistical Inference and Hypothesis Testing in Data Science Applications

    Skills you'll gain: Statistical Hypothesis Testing, Statistical Methods, Probability & Statistics, Data Ethics, Statistical Analysis, Quantitative Research, Statistical Inference, Statistics, Sample Size Determination, Sampling (Statistics)

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    50 reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    R

    Rice University

    Business Applications of Hypothesis Testing and Confidence Interval Estimation

    Skills you'll gain: Statistical Hypothesis Testing, Statistical Methods, Sample Size Determination, Statistical Inference, Estimation, Statistics, Probability & Statistics, Sampling (Statistics), Statistical Analysis, Microsoft Excel, Excel Formulas, Data Analysis, Decision Making

    4.8
    Rating, 4.8 out of 5 stars
    ·
    1.3K reviews

    Mixed · Course · 1 - 4 Weeks

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Hypothesis Testing in Public Health

    Skills you'll gain: Statistical Hypothesis Testing, Biostatistics, Sampling (Statistics), Statistical Inference, Scientific Methods, Quantitative Research, Public Health

    4.8
    Rating, 4.8 out of 5 stars
    ·
    650 reviews

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    S

    SAS

    Introduction to Statistical Analysis: Hypothesis Testing

    Skills you'll gain: Statistical Hypothesis Testing, Statistical Analysis, Correlation Analysis, SAS (Software), Regression Analysis, Statistical Methods, Probability & Statistics, Statistical Modeling, Plot (Graphics), Statistical Inference

    4.7
    Rating, 4.7 out of 5 stars
    ·
    175 reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Data Science: Statistics and Machine Learning

    Skills you'll gain: Shiny (R Package), Rmarkdown, Model Evaluation, Regression Analysis, Exploratory Data Analysis, Statistical Inference, Predictive Modeling, Statistical Hypothesis Testing, Machine Learning Algorithms, Plotly, Interactive Data Visualization, Probability & Statistics, Statistical Machine Learning, Data Presentation, Data Visualization, Statistical Analysis, Statistical Modeling, R Programming, Machine Learning, GitHub

    4.4
    Rating, 4.4 out of 5 stars
    ·
    7.2K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of Amsterdam

    Basic Statistics

    Skills you'll gain: Statistical Hypothesis Testing, Statistics, Scientific Methods, Quantitative Research, Data Analysis Software

    4.6
    Rating, 4.6 out of 5 stars
    ·
    4.6K reviews

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Statistical Inference

    Skills you'll gain: Statistical Inference, Statistical Hypothesis Testing, Probability & Statistics, Probability, Statistics, Bayesian Statistics, Statistical Methods, Statistical Modeling, Statistical Analysis, Probability Distribution, Sampling (Statistics), Sample Size Determination, Data Analysis

    4.2
    Rating, 4.2 out of 5 stars
    ·
    4.5K reviews

    Mixed · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Probability & Statistics for Machine Learning & Data Science

    Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Probability, Statistical Inference, A/B Testing, Statistical Analysis, Statistical Machine Learning, Data Science, Statistical Modeling, Exploratory Data Analysis, Statistical Visualization

    4.6
    Rating, 4.6 out of 5 stars
    ·
    673 reviews

    Intermediate · Course · 1 - 4 Weeks

1234…171

In summary, here are 10 of our most popular hypothesis testing courses

  • The Power of Statistics: Google
  • Statistics Foundations: Meta
  • Probability, Statistical Inference and Regression Analysis: Arizona State University
  • Automate, Analyze, and Evaluate ML Experiments: Coursera
  • Statistical Inference and Hypothesis Testing in Data Science Applications: University of Colorado Boulder
  • Business Applications of Hypothesis Testing and Confidence Interval Estimation : Rice University
  • Hypothesis Testing in Public Health : Johns Hopkins University
  • Introduction to Statistical Analysis: Hypothesis Testing: SAS
  • Data Science: Statistics and Machine Learning: Johns Hopkins University
  • Basic Statistics: University of Amsterdam

Frequently Asked Questions about Hypothesis Testing

Hypothesis testing is a statistical method used to make decisions based on data analysis. It involves formulating a hypothesis, collecting data, and determining whether to accept or reject the hypothesis based on statistical evidence. This process is crucial in various fields, including science, business, and healthcare, as it helps validate assumptions and informs decision-making. By understanding hypothesis testing, individuals can critically evaluate data, leading to more informed choices and strategies.‎

Careers that involve hypothesis testing span various industries, including data analysis, market research, healthcare, and academia. Positions such as data analyst, statistician, research scientist, and quality assurance analyst often require a solid understanding of hypothesis testing. Additionally, roles in product development and marketing analytics also benefit from these skills, as they rely on data-driven decision-making to optimize strategies and outcomes.‎

To effectively learn hypothesis testing, you should focus on developing skills in statistics, data analysis, and critical thinking. Familiarity with statistical software and programming languages, such as Python or R, is also beneficial. Understanding concepts like p-values, confidence intervals, and the null hypothesis is essential. Additionally, practical experience in analyzing real-world data sets will enhance your ability to apply hypothesis testing in various contexts.‎

Some of the best online courses for learning hypothesis testing include Business Applications of Hypothesis Testing and Confidence Interval Estimation and Hypothesis Testing with Python and Excel. These courses provide foundational knowledge and practical applications, making them suitable for learners at different levels. Additionally, specialized courses like Statistical Inference and Hypothesis Testing in Data Science Applications can further enhance your understanding.‎

Yes. You can start learning hypothesis testing on Coursera for free in two ways:

  1. Preview the first module of many hypothesis testing courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in hypothesis testing, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn hypothesis testing, start by exploring online courses that cover the fundamentals. Engage with interactive exercises and real-world examples to reinforce your understanding. Practice analyzing data sets and applying hypothesis testing techniques to various scenarios. Joining study groups or online forums can also provide support and enhance your learning experience through discussion and collaboration.‎

Typical topics covered in hypothesis testing courses include the formulation of null and alternative hypotheses, significance levels, p-values, confidence intervals, and various statistical tests (e.g., t-tests, chi-square tests). Courses may also explore the application of hypothesis testing in different fields, such as business, healthcare, and social sciences, providing a comprehensive understanding of how these concepts are utilized in real-world situations.‎

For training and upskilling employees in hypothesis testing, courses like AI Workflow: Data Analysis and Hypothesis Testing and Hypothesis Testing in Public Health are excellent choices. These courses are designed to equip professionals with the necessary skills to apply hypothesis testing in their respective fields, enhancing their analytical capabilities and decision-making processes.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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