INFERENTIAL STATISTICS TERMS ni LIFE

Sampling & Generalization

Uses a sample to infer insights about a larger population.

Hypothesis Testing

Evaluates claims using statistical significance (e.g., t-tests, ANOVA)

Confidence Intervals

Estimates the likely range of a population parameter based on sample data.

Regression & Correlation

Determines relationships between variables (e.g., spatial dependence in environmental models).

T-test

Used to compare the means of two groups to determine if they are significantly different

Analysis of Variance

Compares the means of three or more groups to identify significant differences.

Correlation analysis

is a statistical technique used to measure the strength and direction of the relationship between two or more variables

One-Way ANOVA

Compares means across one independent variable (e.g., different teaching methods).

Two-Way ANOVA

Examines two independent variables simultaneously (e.g., teaching method & class time).

Repeated Measures ANOVA

Used when the same subjects are tested under different conditions over time

Positive Correlation

Both variables increase or decrease together (e.g., higher study hours lead to higher test scores).

Negative Correlation

One variable increases while the other decreases (e.g., increased exercise leads to lower body weight).

Zero Correlation

No relationship exists between the variables (e.g., shoe size and intelligence

Linear Correlation

The relationship follows a straight-line pattern.

Nonlinear (Curvilinear) Correlation

The relationship follows a curved pattern (e.g., stress and productivity may increase initially but decline at higher stress levels)

Pearson's Correlation Coefficient

is a statistical measure that quantifies the strength and direction of the linear relationship between two variables.

Spearman’s Rank Correlation

is a nonparametric measure of the strength and direction of the monotonic relationship between two variables.

Measures monotonic relationships – If one variable increases, the other tends to increase or decrease consistently. Uses ranked data – Instead of raw values, it compares the relative rankings of observations.

• Assesses the relationship between two ranked (ordinal) variables.

Kendall’s Tau Correlation

is a nonparametric measure of the strength and direction of the association between two ranked variables. It evaluates the ordinal relationship between two datasets, making it useful when data is not normally distributed or contains ties.

• Used for small datasets and measures the association between ranked variables.

Parametric Statistics

Assumes a specific distribution (e.g., normal distribution).

➢Uses fixed parameters like mean and standard deviation.

➢More powerful when assumptions hold (e.g., t-tests, ANOVA, linear regression).

Examples: Pearson’s correlation, z-test, t-test.

Nonparametric Statistics

Does not assume a specific distribution (distribution-free methods).Uses ranks or medians instead of means for analysis.

More flexible, especially for small or skewed datasets (e.g., Mann-Whitney U test, Kruskal-Wallis test).

Examples: Spearman’s correlation, Kendall’s Tau, Wilcoxon test.

Independent t-test

Compares two separate groups (e.g., male vs. female students' math scores).

Paired t-test

Compares two related measurements (e.g., students' test scores before and after tutoring).

One-sample t-test

Compares a sample mean to a known population mean.