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38 lines
1.7 KiB
Markdown
38 lines
1.7 KiB
Markdown
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# Statistical Hypothesis Testing Module (`obistats`)
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This Go package provides implementations of common **t-tests** for comparing sample means under different assumptions. It supports one- and two-sample tests, paired or unpaired designs.
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## Core Types
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- **`TTestResult`**: Encapsulates the outcome of a t-test, including:
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- Sample sizes (`N1`, `N2`)
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- Test statistic value (`T`)
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- Degrees of freedom (`DoF`)
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- Alternative hypothesis type (`AltHypothesis`: `LocationDiffers`, `LocationLess`, or `LocationGreater`)
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- Computed *p*-value (`P`)
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- **`TTestSample` interface**: Requires methods `Weight()`, `Mean()`, and `Variance()` — enabling reuse with summary statistics.
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## Supported Tests
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1. **`TwoSampleTTest(x1, x2)`**
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Standard Student’s *t*-test for two independent samples assuming **equal variances** and normality.
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2. **`TwoSampleWelchTTest(x1, x2)`**
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Welch’s *t*-test for two independent samples **without equal-variance assumption**, using Satterthwaite approximation for degrees of freedom.
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3. **`PairedTTest(x1, x2)`**
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Paired *t*-test for dependent samples (e.g., before/after), testing mean of differences against `μ0`.
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4. **`OneSampleTTest(x)`**
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One-sample *t*-test comparing sample mean to a known population mean `μ0`.
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## Error Handling
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- Returns errors for invalid inputs: zero sample size (`ErrSampleSize`), zero variance (`ErrZeroVariance`), or mismatched paired sample lengths (`ErrMismatchedSamples`).
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## Implementation Notes
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- *p*-values are computed using the cumulative distribution function (CDF) of the Student’s *t*-distribution.
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- Designed for statistical rigor and modularity, reusing internal utilities (e.g., `Mean`, `StdDev`) from a shared module.
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