Statistical Analysis Skill
Biomechanical data statistical analysis with Excel output
Overview
This skill provides a complete workflow for biomechanical research:
- •Paired t-test: Condition and task comparisons with Bonferroni correction
- •Repeated-measures ANOVA: N×M within-subject design analysis
- •Excel output: Formatted reports with standard color scheme and templates
- •Excel formula automation: Auto-updating analysis with hybrid Python + formula approach
When to Use
- •Analyzing EMG, force, or kinematic data
- •Comparing conditions (e.g., new vs old cart)
- •Comparing tasks (e.g., lift, pull, push)
- •Running repeated-measures experiments
- •Generating publication-ready statistical reports in Excel
- •Creating auto-updating Excel analysis sheets
Files
| File | Purpose |
|---|---|
ttest_statistical_analysis.py | Paired t-test function library |
anova_statistical_analysis.py | Repeated-measures ANOVA function library |
excel_utils.py | Common Excel formatting utilities |
excel-format.md | Standard formatting templates (color scheme, sheet structure) |
excel-formula-automation.md | Guide for Excel formula automation workflow |
Key Principle
⚠️ These are FUNCTION LIBRARIES, not standalone scripts.
The AI must:
- •Inspect the data to determine column structure
- •Identify dependent variable and condition factors
- •Call functions with explicit parameters
- •Always output results to Excel
Usage Example
T-test Analysis
python
from ttest_statistical_analysis import (
load_and_preprocess_data,
aggregate_trials,
paired_ttest_condition,
export_to_excel
)
# AI determines these by inspecting data
dependent_var = "rvc_norm_rms"
condition_col = "cart_categories"
condition_values = ["new", "old"]
# Run analysis
df = load_and_preprocess_data(data_path, dependent_var, condition_col)
df_agg = aggregate_trials(df, dependent_var, condition_col)
results = paired_ttest_condition(df_agg, condition_col, condition_values)
ANOVA Analysis
python
from anova_statistical_analysis import (
compute_cell_means,
find_valid_subjects,
run_rm_anova,
export_to_excel
)
# Run analysis
cell_means = compute_cell_means(df, dependent_var, condition_col)
valid_subjects, _ = find_valid_subjects(cell_means, condition_col, dependent_var)
anova_results = run_rm_anova(cell_means_valid, dependent_var, condition_col)
Excel Output Format
All analyses produce Excel files with standard sheets:
- •methods: Analysis methodology (dynamically generated)
- •descriptives: Mean, SD, SEM, N per condition
- •statistical_tests: Test results with significance highlighting
- •cell_means: Subject-level data (optional)
Standard Color Scheme
| Element | Color Code |
|---|---|
| Header Background | #4472C4 (Blue) |
| Header Font | White |
| Significant Cell BG | #C6EFCE (Light Green) |
| Significant Cell Font | #006100 (Dark Green) |
See excel-format.md for detailed formatting specifications.
See excel-formula-automation.md for formula automation workflow.