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- Hypothesis Testing

- Advanced Statistics for Data Science: Johns Hopkins University
- Introduction to Statistics & Data Analysis in Public Health: Imperial College London
- Statistics for Data Science with Python: IBM Skills Network
- Six Sigma Tools for Improve and Control: University System of Georgia
- Six Sigma Advanced Analyze Phase: University System of Georgia
- Population Health: Responsible Data Analysis: Universiteit Leiden
- Data Science with R - Capstone Project: IBM Skills Network
- Stability and Capability in Quality Improvement: University of Colorado Boulder
- Topics in Applied Econometrics: Queen Mary University of London
- Logistic Regression in R for Public Health: Imperial College London

While hypothesis might make you think of science, hypothesis testing is a mathematical process that involves testing data using statistics to see if there is enough evidence to support a hypothesis. A hypothesis is a belief or a proposed explanation for something that has not yet been backed up by evidence. Hypothesis testing takes the data gathered in an experiment, survey, or other collection of information and interprets it. The five steps of hypothesis testing are specifying the null hypothesis (the statement of no relationship between the factors involved), specifying the alternative hypothesis (the statement that there is a relationship between the factors involved), setting the significance level (the percentage of chance the alternative hypothesis will be accepted), calculating the test statistic and corresponding P-value (probability of obtaining the sample statistic), and drawing a conclusion.

Learning hypothesis testing is important because it's what allows you to decide if something is true based on real data. If you're in marketing, for example, you can use hypothesis testing in consumer research to see how well your product is accepted by customers. If you're in the medical field, you can use it to see if a treatment has positive effects. An educational institution may be interested in determining if getting eight hours of sleep correlates with higher grades for its students. Hypothesis testing helps remove the likelihood of chance affecting a conclusion and instead backs it up with statistically significant data.

Online courses on Coursera can help you learn how hypothesis testing applies to a variety of fields, including health care, business, artificial intelligence, psychology, social sciences, and machine learning. You can learn how to use specific statistical tools to conduct hypothesis testing, too, such as R, RStudio for Six Sigma, and Python. With online courses on Coursera, you also have the opportunity to learn the essential building blocks of hypothesis testing, which include choosing the right hypothesis testing tool and performing hypothesis tests using chi-square tests, correlation, t-tests, simple regression, logistic regression, and analysis of variance (ANOVA).

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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