Confidence Intervals and Hypothesis Tests
- Overview
- Course Content
- Requirements & Materials
Confidence Intervals and Hypothesis Tests
Course Description
This course covers two important methodologies in statistics – confidence intervals and hypothesis testing.
Confidence intervals are encountered in everyday life, and allow us to make probabilistic statements such as: “Based on the sample of observations we conducted, we are 95% sure that the unknown mean lies between A and B,” and “We are 95% sure that Candidate Smith’s popularity is 52% +/- 3%.” We begin the course by discussing what a confidence interval is and how it is used. We then formulate and interpret confidence intervals for a variety of probability distributions and their parameters.
Hypothesis testing allows us to pose hypotheses and test their validity in a statistically rigorous way. For instance, “Does a new drug result in a higher cure rate than the old drug” or “Is the mean tensile strength of item A greater than that of item B?” The second half the course begins by motivating hypothesis tests and how they are used. We then discuss with the types of errors that can occur with hypothesis testing, and how to design tests to mitigate those errors. Finally, we formulate and interpret hypothesis tests for a variety of probability distributions and their parameters.
Hypothesis testing allows us to pose hypotheses and test their validity in a statistically rigorous way. For instance, “Does a new drug result in a higher cure rate than the old drug” or “Is the mean tensile strength of item A greater than that of item B?” The second half the course begins by motivating hypothesis tests and how they are used. We then discuss with the types of errors that can occur with hypothesis testing, and how to design tests to mitigate those errors. Finally, we formulate and interpret hypothesis tests for a variety of probability distributions and their parameters.
CONFIDENCE INTERVALS
HYPOTHESIS TESTING
Prerequisites
Recommended
- Prior experience in Excel and/or the R freeware statistical package, or similar applications
Required
- Introductory knowledge in theory and basic calculus, as well as the material from the previous courses in this series (A Gentle Introduction to Probability, Random Variables, and A Gentle Introduction to Statistics).
Materials
Required
- Internet connection (DSL, LAN, or cable connection desirable)
- Adobe Acrobat PDF reader (to download for free, visit get.adobe.com/reader/)
- R statistical software (to download for free, visit cran.r-project.org) (or similar statistics packages such as Minitab, JMP, SAS, etc.)
- Excel (or equivalent)
- Bonus software: Any “real,” high-level language such as MATLAB, Python, etc.
Provided
- All content and course materials available online
Who Should Attend
This course is designed for anyone who needs the tools to understand data, science, philosophy, engineering, economics, and finance.
What You Will Learn
- What a confidence interval is and how it is used
- How to formulate and interpret confidence intervals for a variety of probability distributions and their parameters
- What a hypothesis test is and how it is used
- The types of errors that can occur with hypothesis testing, and how to design tests to mitigate those errors
- How to formulate and interpret hypothesis tests for a variety of probability distributions and their parameters
How You Will Benefit
- Formulate and interpret hypothesis tests for a variety of probability distributions and their parameters.
- Become familiar with the types of errors that can occur with hypothesis testing, and how to design tests to mitigate those errors.
- Formulate and interpret confidence intervals for a variety of probability distributions and their parameters.
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Taught by Experts in the Field
The course schedule was well-structured with a mix of lectures, class discussions, and hands-on exercises led by knowledgeable and engaging instructors.
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