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Two Variances - Graphing Calculator definitions
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Two-Sample F Test
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Two-Sample F Test
A statistical procedure comparing two sample variances to assess differences between population variances using an F statistic.
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Terms in this set (15)
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Two-Sample F Test
A statistical procedure comparing two sample variances to assess differences between population variances using an F statistic.
Variance
A measure of data spread indicating how much values differ from the mean within a sample or population.
Standard Deviation
A value quantifying the average distance of data points from the mean, used to summarize variability.
F Statistic
A ratio of two sample variances used to evaluate whether population variances are significantly different.
P-Value
A probability indicating the strength of evidence against the null hypothesis; lower values suggest stronger evidence.
Null Hypothesis
A statement asserting no difference between population variances, often used as a baseline for testing claims.
Alternative Hypothesis
A statement reflecting the claim being tested, such as one population variance being greater than another.
Significance Level
A threshold, commonly denoted as alpha, determining when to reject the null hypothesis based on the p-value.
Sample Size
The number of observations in each group, affecting the reliability and calculation of statistical tests.
Graphing Calculator
A device used to input data or statistics and automate calculations for hypothesis testing, including F tests.
Right-Tailed Test
A hypothesis test where the alternative hypothesis claims one variance is greater than the other, focusing on the upper tail.
Data Tab
A calculator menu option for entering raw data lists to perform statistical tests directly from sample values.
Stats Tab
A calculator menu option for inputting summary statistics, such as sample standard deviations and sizes, for hypothesis testing.
Alpha
A symbol representing the significance level, often set at 0.05, guiding decision-making in hypothesis tests.
Sample Statistics
Numerical summaries, such as standard deviations and sizes, used to perform statistical tests without raw data.