Decision Making With Data

When attempting to derive insights from data, it is often impractical or even impossible to examine every element of a population. Thus, the ability to draw valid inferences from sample data is a crucial skill for data analysts.

In this module, you will learn about using interval estimates to estimate population parameters, explore key concepts in statistical testing and statistical decision making, and discover the role that sample size plays in the precision of your interval estimates and the power of your statistical tests.

Estimated time to complete this module: 4 to 5 hours

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https://share.vidyard.com/watch/Nu4kAKRWVDtXx84UicUSWQ

Specific topics covered in this module include:

Estimation

Introduction to Statistical Inference
What Is a Confidence Interval?
Estimating a Mean
Visualizing Sampling Variation
Constructing Confidence Intervals
Understanding the Confidence Level and Alpha Risk
Prediction Intervals
Tolerance Intervals
Comparing Interval Estimates

Foundations in Statistical Testing

Introduction to Statistical Testing
Statistical Decision-Making
Understanding the Null and Alternative Hypotheses
Sampling Distribution under the Null
The p-Value and Statistical Significance

Hypothesis Testing for Continuous Data

Conducting a One-Sample t Test
Understanding p-Values and t Ratios
Equivalence Testing
Comparing Two Means
Unequal Variances Tests
Paired Observations
One-Way ANOVA (Analysis of Variance)
Multiple Comparisons
Statistical Versus Practical Significance

Sample Size and Power

Introduction to Sample Size and Power
Sample Size for a Confidence Interval for the Mean
Outcomes of Statistical Tests
Statistical Power
Exploring Sample Size and Power
Calculating the Sample Size for One-Sample t Tests
Calculating the Sample Size for Two-Sample t Tests and ANOVA