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

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