# Statistical & Hypothesis Tests: Using the One-sample T-test

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• 11 Videos | 1h 42m 22s
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One-sample T-tests are probably the single most commonly used type of hypothesis test. Through this course, learn to manually implement the one-sample T-test to know exactly how the p-value and test statistic are calculated. You'll examine various library implementations of the one-sample T-test and apply the test on data drawn from several different distributions. This course will also help you explore the non-parametric Wilcoxon signed-rank test, which is conceptually very similar to the one-sample T-test and helps estimate the median rather than the mean of that population without making assumptions about the population distribution. Upon completion of this course, you will be able to use the one-sample T-test as well as its non-parametric equivalent to evaluate both one-sided and two-sided hypotheses about the population mean or median.

## WHAT YOU WILL LEARN

• discover the key concepts covered in this course
install various modules in python
create a function to manually perform a T-test
compare a manual one-sample T-test to a built-in test
explore Laplace and Wald distributions with T-tests
test data to see if it is normally distributed using Shapiro-Wilk and Anderson-Darling tests
• perform T-tests on real-world data
explore one-sided and two-sided T-tests
perform the Wilcoxon signed-rank test to compare medians
test medians using the Wilcoxon signed-rank test
summarize the key concepts covered in this course

## IN THIS COURSE

• 1.
Course Overview
• 2.
Installing Modules
• 3.
Setting up a Manual One-sample T-test
• 4.
Performing T-tests Using Different Libraries
• 5.
Performing T-tests on Data with Different Distributions
• 6.
Testing for Normal Distributions Using Statistical Tests
• 7.
Exploring T-tests with Real-world Examples
• 8.
Using Single-sided T-tests
• 9.
Running the Wilcoxon Signed-rank Test
• 10.
Comparing Medians Using the Wilcoxon Signed-rank Test
• 11.
Course Summary

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