# Statistical & Hypothesis Tests: Getting Started with Hypothesis Testing

Statistics    |    Intermediate
• 9 videos | 52m 4s
• Includes Assessment
Rating 4.7 of 28 users (28)
Hypothesis testing is the bedrock of inferential statistics, allowing us to draw inferences reliably about the population as a whole. Use this course to learn more about the distinction between descriptive and inferential statistics and how the latter seek to generalize from the sample to the population as a whole. Examine the components of a typical hypothesis test, such as the null and alternative hypothesis, the test statistic, and the p-value. You'll also explore type-I and type-II errors and the use cases and conceptual underpinnings of t-tests and ANOVA. By the time you finish this course, you will be able to identify use-cases for hypothesis testing and conceptually construct the appropriate null and alternative hypotheses for such tests.

## WHAT YOU WILL LEARN

• Discover the key concepts covered in this course
Outline how descriptive and inferential statistics work
Describe the fundamentals of hypothesis testing
Set up null and alternative hypotheses for statistical tests
Interpret p-values using alpha levels
• Explore the one-sample, two-sample, and paired-sample t-tests
Compare and contrast type i and type ii errors in hypothesis testing
Apply the anova test for multiple groups
Summarize the key concepts covered in this course

## IN THIS COURSE

• 3.  Introducing Hypothesis Testing
• 4.  The Null Hypothesis and the Alternative Hypothesis
• 5.  P-values and Alpha Levels
• 6.  Introducing T-tests
• 7.  Errors in Hypothesis Testing
• 8.  Performing ANOVA Analysis
• 9.  Course Summary

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