Statistical & Hypothesis Tests: Getting Started with Hypothesis Testing

Statistics    |    Intermediate
  • 9 videos | 52m 4s
  • Includes Assessment
  • Earns a Badge
Rating 4.7 of 28 users 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

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