mirror of
https://github.com/metabarcoding/obitools4.git
synced 2026-04-30 12:00:39 +00:00
Eric Coissac
8c7017a99d
- Update obioptions.Version from "Release 4.4.29" to "/v/ Release v5" - Update version.txt from 4.29 → .30 (automated by Makefile)
38 lines
1.7 KiB
Markdown
38 lines
1.7 KiB
Markdown
# Statistical Hypothesis Testing Module (`obistats`)
|
||
|
||
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.
|
||
|
||
## Core Types
|
||
|
||
- **`TTestResult`**: Encapsulates the outcome of a t-test, including:
|
||
- Sample sizes (`N1`, `N2`)
|
||
- Test statistic value (`T`)
|
||
- Degrees of freedom (`DoF`)
|
||
- Alternative hypothesis type (`AltHypothesis`: `LocationDiffers`, `LocationLess`, or `LocationGreater`)
|
||
- Computed *p*-value (`P`)
|
||
|
||
- **`TTestSample` interface**: Requires methods `Weight()`, `Mean()`, and `Variance()` — enabling reuse with summary statistics.
|
||
|
||
## Supported Tests
|
||
|
||
1. **`TwoSampleTTest(x1, x2)`**
|
||
Standard Student’s *t*-test for two independent samples assuming **equal variances** and normality.
|
||
|
||
2. **`TwoSampleWelchTTest(x1, x2)`**
|
||
Welch’s *t*-test for two independent samples **without equal-variance assumption**, using Satterthwaite approximation for degrees of freedom.
|
||
|
||
3. **`PairedTTest(x1, x2)`**
|
||
Paired *t*-test for dependent samples (e.g., before/after), testing mean of differences against `μ0`.
|
||
|
||
4. **`OneSampleTTest(x)`**
|
||
One-sample *t*-test comparing sample mean to a known population mean `μ0`.
|
||
|
||
## Error Handling
|
||
|
||
- Returns errors for invalid inputs: zero sample size (`ErrSampleSize`), zero variance (`ErrZeroVariance`), or mismatched paired sample lengths (`ErrMismatchedSamples`).
|
||
|
||
## Implementation Notes
|
||
|
||
- *p*-values are computed using the cumulative distribution function (CDF) of the Student’s *t*-distribution.
|
||
- Designed for statistical rigor and modularity, reusing internal utilities (e.g., `Mean`, `StdDev`) from a shared module.
|