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Hypothesis Testing Terms
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T-Test
A statistical test used to compare the means of two groups or to compare a sample mean to a population mean when variances are unknown and/or sample size is small.
P-value
The probability of obtaining test results at least as extreme as the results observed, assuming that the null hypothesis is true.
Confidence Interval
A range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter.
Statistical Power
The probability that the test correctly rejects a false null hypothesis (1 - β), where β is the probability of a Type II error.
Type I Error
Occurs when the null hypothesis is wrongly rejected when it is actually true (false positive).
One-Tailed Test
A hypothesis test in which the alternative hypothesis specifies either an increase or a decrease in a value (but not both).
Critical Value
The value that the test statistic must exceed in order to reject the null hypothesis.
Two-Tailed Test
A hypothesis test in which the alternative hypothesis does not specify the direction of the effect or difference.
Z-Test
A statistical test used to determine whether two population means are different when the variances are known and the sample size is large.
Degrees of Freedom
The number of independent values that can vary in the calculation of a statistic, usually related to the number of subjects in the sample minus the number of parameters estimated.
Null Hypothesis
A statement that there is no effect or no difference, and it is the hypothesis that researchers typically try to disprove.
Type II Error
Occurs when the null hypothesis is wrongly not rejected when it is false (false negative).
Significance Level
The threshold below which the p-value must fall for us to reject the null hypothesis.
Alternative Hypothesis
A statement that indicates the presence of an effect or difference. If evidence supports this, the null hypothesis may be rejected.
Test Statistic
A standardized value that is calculated from sample data during a hypothesis test.
Chi-Square Test
A statistical test used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories.
ANOVA (Analysis of Variance)
A statistical test that compares the means of three or more independent groups to see if at least one mean is different from the others.
Post-hoc Analysis
Additional analyses performed after a significant finding has been reported, to explore other aspects of the data or perform multiple comparisons.
Bonferroni Correction
A statistical adjustment for multiple comparisons that lowers the alpha level to reduce the likelihood of Type I errors.
Effect Size
A quantitative measure of the magnitude of the experimental effect. The larger the effect size, the stronger the relationship between two variables.
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