Statistical Tests and Analysis of Variance
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A series of flashcards covering key concepts related to statistical tests and analyses of variance from the lecture notes.
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40 Terms
Chi-Square Test
A statistical method used to determine if there is a significant association between categorical variables.
Factor
A categorical variable that can take on a limited number of values, treated as levels in statistical analysis.
Phi coefficient
An effect size measure used for 2x2 contingency tables in chi-square tests.
Normality Assumption
The assumption that the data follows a normal distribution, required for certain statistical tests.
Histograms
Graphical representations of data distributions that show frequency of data points within specified intervals.
Q-Q Plots
Quantile-Quantile plots used to assess if a dataset follows a specified distribution.
Levene’s test
A statistical test used to assess the equality of variances across groups.
Shapiro-Wilk Test
A test aimed at checking the normality of data.
Paired-Samples T-Test
A statistical test comparing means from the same group at different times.
Effect Size
A quantitative measure of the magnitude of a phenomenon, often reported alongside p-values.
ANOVA (Analysis of Variance)
A statistical method for comparing means across multiple groups to determine if at least one differs.
Post-hoc Tests
Tests conducted after an ANOVA to determine which specific group means are significantly different.
Bonferroni Correction
A statistical procedure to adjust significance levels when multiple comparisons are made, reducing the chance of Type I error.
Kruskal-Wallis Test
A non-parametric test used to compare three or more independent groups.
Homogeneity of Variance
The assumption that different samples have the same variance, critical for certain statistical tests.
Type I Error
The incorrect rejection of a true null hypothesis, also known as a false positive.
Type II Error
The failure to reject a false null hypothesis, known as a false negative.
Chi-Square Test
A statistical method used to determine if there is a significant association between categorical variables.
Factor
A categorical variable that can take on a limited number of values, treated as levels in statistical analysis.
Phi coefficient
An effect size measure used for 2x2 contingency tables in chi-square tests.
Normality Assumption
The assumption that the data follows a normal distribution, required for certain statistical tests.
Histograms
Graphical representations of data distributions that show frequency of data points within specified intervals.
Q-Q Plots
Quantile-Quantile plots used to assess if a dataset follows a specified distribution.
Levene
’s test
A statistical test used to assess the equality of variances across groups.
Shapiro-Wilk Test
A test aimed at checking the normality of data.
Paired-Samples T-Test
A statistical test comparing means from the same group at different times.
Effect Size
A quantitative measure of the magnitude of a phenomenon, often reported alongside p-values.
ANOVA (Analysis of Variance)
A statistical method for comparing means across multiple groups to determine if at least one differs.
Post-hoc Tests
Tests conducted after an ANOVA to determine which specific group means are significantly different.
Bonferroni Correction
A statistical procedure to adjust significance levels when multiple comparisons are made, reducing the chance of Type I error.
Kruskal-Wallis Test
A non-parametric test used to compare three or more independent groups.
Homogeneity of Variance
The assumption that different samples have the same variance, critical for certain statistical tests.
Type I Error
The incorrect rejection of a true null hypothesis, also known as a false positive.
Type II Error
The failure to reject a false null hypothesis, known as a false negative.
Null Hypothesis (H_0)
A statement that there is no effect or no difference between groups or variables.
Alternative Hypothesis (H1 or Ha)
A statement that there is an effect or a difference between groups or variables, contrary to the null hypothesis.
P-value
The probability of observing a test statistic as extreme as, or more extreme than, the one observed, assuming the null hypothesis is true.
Degrees of Freedom (df)
The number of independent values or quantities that can be varied in a statistical analysis without violating any constraints.
Cramer's V
An effect size measure used for chi-square tests in contingency tables larger than 2x2, indicating the strength of association between two categorical variables.
Cohen's d
An effect size measure used for t-tests, representing the standardized difference between two means.
Note 
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