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Probability and Statistics IV: Confidence Intervals and Hypothesis Tests

Name: Probability and Statistics IV: Confidence Intervals and Hypothesis Tests
Price: 199 USD
Availability: InStock

114 studentsINTERMEDIATEEnglish

Korshub's take

Confidence intervals and hypothesis tests are the two pillars of statistical inference. This course teaches both, grounding abstract concepts in concrete examples: "we're 95% sure the parameter lies here" and "does the evidence refute this hypothesis?"

Confidence intervals come first. You'll learn to interpret them (they're not what most people think), then formulate them for different distributions. Then hypothesis testing: how to frame a claim, how to rigorously test it, and what error types matter (false positives and false negatives have different costs).

The second half is applying these tests to real data and distributions. Each method has a different purpose and assumptions; choosing the wrong test can hide a real signal or manufacture false certainty. By course end, you'll design tests that match your questions and interpret results with appropriate caution. For statisticians, researchers, and analysts who need rigor in their claims.

About this course

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 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.