Two samples with sizes n1=980n_1=980 and n2=1125n_2=1125 are being compared using an FF test for equality of variances. Is it appropriate to ignore the assumption of normality due to the large sample sizes?

No, because normality is still required for the FF test.

Yes, because the FF test is robust for large samples.

No, because the FF test is only valid for equal sample sizes.

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1. Intro to Stats and Collecting Data(0)

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Levels of Measurement
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Intro to Collecting Data
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Sampling Methods
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2. Describing Data with Tables and Graphs(0)

Visualizing Qualitative vs. Quantitative Data
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Frequency Distributions
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Histograms
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Bar Graphs and Pareto Charts
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Pie Charts
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Frequency Polygons
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Dot Plots
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Stemplots (Stem-and-Leaf Plots)
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Time-Series Graph
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3. Describing Data Numerically(0)

Mean
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Median
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Mode
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Standard Deviation
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Interpreting Standard Deviation
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Percentiles & Quartiles
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Boxplots
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4. Probability(0)

Basic Concepts of Probability
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Complements
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Addition Rule
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Multiplication Rule: Independent Events
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Introduction to Contingency Tables
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Multiplication Rule: Dependent Events
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Bayes' Theorem
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Fundamental Counting Principle
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Counting
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5. Binomial Distribution & Discrete Random Variables(0)

Discrete Random Variables
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Binomial Distribution
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Poisson Distribution
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Hypergeometric Distribution
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6. Normal Distribution and Continuous Random Variables(0)

Uniform Distribution
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Standard Normal Distribution
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Non-Standard Normal Distribution
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7. Sampling Distributions & Confidence Intervals: Mean(0)

Sampling Distribution of the Sample Mean and Central Limit Theorem
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Introduction to Confidence Intervals
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Confidence Intervals for Population Mean
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Determining the Minimum Sample Size Required
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8. Sampling Distributions & Confidence Intervals: Proportion(0)

Sampling Distribution of Sample Proportion
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Confidence Intervals for Population Proportion
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Chi Square Distribution
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Confidence Intervals for Population Variance
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9. Hypothesis Testing for One Sample(0)

Steps in Hypothesis Testing
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Performing Hypothesis Tests: Means
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Performing Hypothesis Tests: Proportions
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Performing Hypothesis Tests: Variance
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Critical Values and Rejection Regions
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Link Between Confidence Intervals and Hypothesis Testing
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Type I & Type II Errors
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10. Hypothesis Testing for Two Samples(0)

Two Proportions
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Two Means - Unknown, Unequal Variance
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Two Means - Unknown, Equal Variance
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Two Means - Known Variance
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Two Means - Matched Pairs (Dependent Samples)
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Two Variances and F Distribution
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11. Correlation(0)

Scatterplots & Intro to Correlation
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Correlation Coefficient
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12. Regression(0)

Linear Regression & Least Squares Method
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Prediction Intervals
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13. Chi-Square Tests & Goodness of Fit(0)

Goodness of Fit Test
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Contingency Tables
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Independence Tests
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Homogeneity Tests
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14. ANOVA(0)

Introduction to ANOVA
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Multiple Comparisons: Tukey Test
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Multiple Comparisons: Tukey-Kramer Test
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Multiple Comparisons: Bonferoni Test
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Two-Way ANOVA
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10. Hypothesis Testing for Two Samples

Two Variances and F Distribution

10. Hypothesis Testing for Two Samples

Two Variances and F Distribution: Videos & Practice Problems

Video Lessons Practice Worksheet

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Problem 4Multiple Choice

Two samples with sizes $n sub 1 equals 980$ and $n sub 2 equals 1125$ are being compared using an $F$ test for equality of variances. Is it appropriate to ignore the assumption of normality due to the large sample sizes?

Yes, because the $F$ test is robust for large samples.

No, because normality is still required for the $F$ test.

Yes, because the Central Limit Theorem applies to variances.

No, because the $F$ test is only valid for equal sample sizes.

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