- 1. Introduction to Statistics53m
- 2. Describing Data with Tables and Graphs2h 1m
- 3. Describing Data Numerically2h 8m
- 4. Probability2h 26m
- 5. Binomial Distribution & Discrete Random Variables3h 28m
- 6. Normal Distribution & Continuous Random Variables2h 21m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 37m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals22m
- Confidence Intervals for Population Mean1h 26m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion2h 20m
- 9. Hypothesis Testing for One Sample5h 13m
- Steps in Hypothesis Testing1h 13m
- Performing Hypothesis Tests: Means1h 1m
- Hypothesis Testing: Means - Excel42m
- Performing Hypothesis Tests: Proportions39m
- Hypothesis Testing: Proportions - Excel27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions29m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors15m
- 10. Hypothesis Testing for Two Samples4h 49m
- Two Proportions1h 12m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 2m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- 11. Correlation1h 24m
- 12. Regression3h 42m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - Excel8m
- Finding Residuals and Creating Residual Plots - Excel11m
- Inferences for Slope32m
- Enabling Data Analysis Toolpak1m
- Regression Readout of the Data Analysis Toolpak - Excel21m
- Prediction Intervals13m
- Prediction Intervals - Excel19m
- Multiple Regression - Excel29m
- Quadratic Regression23m
- Quadratic Regression - Excel10m
- 13. Chi-Square Tests & Goodness of Fit2h 31m
- 14. ANOVA2h 1m
Type I & Type II Errors: Videos & Practice Problems
Type I & Type II Errors

Describe a Type I & Type II Error for each scenario.
A snack company guarantees that each bag contains at least 200 g of chips.
Type I: We conclude that some bags don't have at least 200g when they all do.
Type II: We conclude that some bags don't have at least 200 g when they all do.
Type I: We conclude all bags have at least 200g when some don't
Type II: We conclude all bags have at least 200g when some don't
Type I: We conclude that all bags have at least 200g when some don't.
Type II: We conclude that some bags don't have at least 200g when they all do.
Type I: We conclude that some bags don't have at least 200g when they all do.
Type II: We conclude all bags have at least 200g when some don't.
Describe a Type I & Type II Error for each scenario.
A computer repair store advertises the average repair cost as \$75 or less.
Type I: We conclude the average repair cost is more than \)75 when it's actually \)75 or less.
Type II: We conclude the average repair cost is \)75 or less when it's more than that.
Type I: We conclude the average repair cost is more than \)75 when it's actually \)75 or less.
Type II: We conclude the average repair cost is more than \)75 when it's actually \)75 or less.
Type I: We conclude the average repair cost is \)75 when it's actually more than \$75.
Type II: We conclude the average repair cost is \)75 or less when it's actually \)75 or less.
Type I: We conclude the average repair cost is \)75 or less when it's more than that.
Type II: We conclude the average repair cost is \)75 or less when it's more than that.
A furniture manufacturer claims that the mean production cost of a dining chair is \$50. Management wants to test if the cost has increased. What are the Type I & Type II Errors? Which is more serious?
Type I: We conclude that μ > 50 when actually μ = 50.
Type II: We conclude that μ = 50 when actually μ > 50.
Type I is more serious.
Type I: We conclude that μ = 50 when actually μ > 50.
Type II: We conclude that μ > 50 when actually μ = 50.
Type I is more serious.
Type I: We conclude that μ > 50 when actually μ = 50.
Type II: We conclude that μ = 50 when actually μ > 50.
Type II is more serious.
Type I: We conclude that μ = 50 when actually μ > 50.
Type II: We conclude that μ > 50 when actually μ = 50.
Type II is more serious.