Statistical Terminology Flashcards
t-statistic:
A t-statistic is a value calculated from a t-test that measures the difference between an observed sample statistic and its hypothesized population parameter in units of standard error. It is used to determine the statistical significance of the difference between two means or a mean and a known value.
One-sample t-test:
one-sample t-test is used to compare a sample mean to a known population mean to determine if there is a significant difference between them.
Paired-samples t-test:
A paired-samples t-test, also known as a dependent samples t-test, is used to compare the means of two related groups or the same group measured at two different time points. It tests whether the mean difference between the paired observations is significantly different from zero.
Cohen’s d:
Cohen’s d is a measure of effect size that quantifies the magnitude of the difference between two means in terms of standard deviation units. It is calculated by dividing the difference between two means by the pooled standard deviation. Values of 0.2, 0.5, and 0.8 are generally considered small, medium, and large effects, respectively.
F-statistic:
An F-statistic is a value calculated from an ANOVA (Analysis of Variance) that measures the ratio of the variance between groups to the variance within groups. It is used to determine whether there are significant differences between the means of three or more groups.
Repeated measures ANOVA:
A repeated measures ANOVA is used when the same participants are measured on the same dependent variable under different conditions or at different time points. It accounts for the non-independence of observations due to the repeated measurements on the same individuals.
Simple ANOVA:
A simple ANOVA, also known as a one-way ANOVA, is used to compare the means of three or more independent groups on a single dependent variable. It tests whether there are significant differences between the group means.
Split-plot ANOVA:
A split-plot ANOVA, also known as a mixed ANOVA, is used when there are both between-subjects and within-subjects factors in the study design. It tests for main effects and interactions between the factors, while accounting for the repeated measurements on the same individuals.
Partial eta squared (ηp²):
Partial eta squared is a measure of effect size used in ANOVA that indicates the proportion of variance in the dependent variable that is explained by an independent variable, while controlling for other independent variables in the model. It ranges from 0 to 1, with higher values indicating a greater effect size.
- A small effect size (η²ₚ ≥ 0.01) indicates that the independent variable accounts for 1% of the variance in the dependent variable.
- A medium effect size (η²ₚ ≥ 0.06) indicates that the independent variable accounts for 6% of the variance.
A large effect size (η²ₚ ≥ 0.14) indicates that the independent variable accounts for 14% or more of the variance.
Pearson’s r:
Pearson’s r, also known as Pearson’s correlation coefficient, is a measure of the linear relationship between two continuous variables. It ranges from -1 to +1, where -1 indicates a perfect negative linear relationship, +1 indicates a perfect positive linear relationship, and 0 indicates no linear relationship. The strength of the relationship is indicated by the absolute value of r, with values closer to 1 (either positive or negative) indicating a stronger linear relationship. Pearson’s r assumes that the data are normally distributed and have a linear relationship.
Type 1 vs. Type 2 error:
A Type 1 error (false positive) occurs when a null hypothesis is rejected when it is actually true. A Type 2 error (false negative) occurs when a null hypothesis is not rejected when it is actually false. The significance level (alpha) is related to the probability of making a Type 1 error, while statistical power is related to the probability of avoiding a Type 2 error.
Post-hoc testing:
Post-hoc tests are conducted after a significant overall ANOVA result to determine which specific group means differ from each other. Common post-hoc tests include Tukey’s HSD, Bonferroni, and Scheffe’s test. These tests control for the increased risk of Type 1 errors that occurs when making multiple pairwise comparisons.
- 95% confidence interval
- A 95% confidence interval is a range of values that is likely to contain the true population parameter 95% of the time.
- In other words, if you were to take many random samples from a population and calculate a 95% confidence interval for each sample, 95% of those intervals would contain the true population value.
- The key things to understand about 95% confidence intervals are:
- They provide a range of plausible values for an unknown population parameter.
- They quantify the uncertainty around an estimate.
- A narrower interval indicates more precision, while a wider interval indicates more uncertainty.
- If the interval does not contain a certain value, it suggests the true value is likely higher or lower than that.
The width of the interval gives a sense of the practical significance of the finding.
