T-Tests

Compare means between groups

T-Tests

T-tests compare means between groups to determine if differences are statistically significant.

Types of T-Tests

Independent Samples T-Test

Compare means between two separate groups.

Example: Compare test scores between students in different schools.

Data Requirements:

  • Data Column: Numeric variable to compare
  • Group Column: Categorical variable with exactly 2 groups
  • Sample Size: At least 2 observations per group

Paired Samples T-Test

Compare means of related observations.

Example: Before/after treatment measurements.

Data Requirements:

  • Column 1: First set of measurements
  • Column 2: Second set of measurements (paired)
  • Sample Size: At least 2 pairs

One-Sample T-Test

Test if a single group differs from a hypothesized value.

Example: Test if a class average differs from the national average.

Data Requirements:

  • Column: Numeric variable
  • Population Mean: Hypothesized value (default: 0)
  • Sample Size: At least 2 observations

How to Run

  1. Select “T-Test” from the test dropdown
  2. Choose the test type (Independent, Paired, One-sample)
  3. Select your data columns
  4. Click “Run Test”

Interpreting Results

Key Statistics

  • t-statistic: Measure of difference relative to variability
  • p-value: Probability of observing this difference by chance
  • Degrees of Freedom: Number of independent observations
  • Group Means: Average values for each group

Statistical Significance

  • p < 0.05: Generally significant
  • p < 0.01: Strong significance
  • p < 0.001: Very strong significance

Assumptions

  • Normality: Data approximately follows normal distribution
  • Independence: Observations are independent (except paired tests)
  • Equal Variance: Groups have similar variability (independent tests)

Export Options

  • LaTeX: Copy-paste ready tables
  • CSV: Structured data files

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