Online Statistical Hypothesis Testing Tool

Statistical Hypothesis Testing Tool

Perform t-tests and chi-squared tests to analyze your data.

Data Input

Enter numerical data for two independent groups, separated by commas.

T-Test Results

Your t-test results will appear here.

Critical T-Value (p=0.05): ${results.criticalT}

Conclusion:

The result is statistically ${results.isSignificant ? 'significant' : 'not significant'} at p < 0.05.

This means there is ${results.isSignificant ? 'a' : 'no'} significant difference between the means of the two groups (Mean1=${results.mean1.toFixed(2)}, Mean2=${results.mean2.toFixed(2)}).

`; }; // --- CHI-SQUARED LOGIC --- runChiTestBtn.addEventListener('click', () => { const dataStr = document.getElementById('chi-test-data').value.trim(); const rows = dataStr.split(/[\n;]+/).map(row => parseData(row)); if (rows.length < 2 || rows[0].length < 2 || rows.some(r => r.length !== rows[0].length)) { chiTestResultsDiv.innerHTML = '

Error: Data must be a table with at least 2 rows and 2 columns.

'; return; } const numRows = rows.length; const numCols = rows[0].length; const rowTotals = rows.map(sum); const colTotals = Array(numCols).fill(0).map((_, i) => sum(rows.map(r => r[i]))); const grandTotal = sum(rowTotals); let chiSquared = 0; const expected = []; for (let r = 0; r < numRows; r++) { expected[r] = []; for (let c = 0; c < numCols; c++) { const expectedVal = (rowTotals[r] * colTotals[c]) / grandTotal; expected[r][c] = expectedVal; chiSquared += ((rows[r][c] - expectedVal) ** 2) / expectedVal; } } const df = (numRows - 1) * (numCols - 1); if (df <= 0) { chiTestResultsDiv.innerHTML = '

Error: Degrees of freedom must be greater than 0.

'; return; } const chi_crit_05 = { 1: 3.84, 2: 5.99, 3: 7.81, 4: 9.49, 5: 11.07, 10: 18.31, 20: 31.41 }; const criticalChi = chi_crit_05[Object.keys(chi_crit_05).reverse().find(key => df >= key)] || 3.84; const isSignificant = chiSquared > criticalChi; lastResults = { type: 'Chi-Squared Test', data: { chiSquared, df, criticalChi, isSignificant } }; renderChiTestResults(lastResults.data); }); const renderChiTestResults = (results) => { chiTestResultsDiv.innerHTML = `

Chi-Squared (χ²) Value: ${results.chiSquared.toFixed(3)}

Degrees of Freedom (df): ${results.df}

Critical χ² Value (p=0.05): ${results.criticalChi}

Conclusion:

The result is statistically ${results.isSignificant ? 'significant' : 'not significant'} at p < 0.05.

This means there is ${results.isSignificant ? 'a' : 'no'} significant association between the categorical variables.

`; }; // --- PDF DOWNLOAD --- downloadPdfBtn.addEventListener('click', () => { if (!lastResults.type) { alert("Please run a test before downloading results."); return; } const { jsPDF } = window.jspdf; const doc = new jsPDF(); doc.setFontSize(22); doc.text('Hypothesis Test Results', 105, 20, { align: 'center' }); doc.setFontSize(16); doc.text(lastResults.type, 105, 28, { align: 'center' }); if (lastResults.type === 'T-Test') { const { tStatistic, df, criticalT, isSignificant, mean1, mean2, n1, n2 } = lastResults.data; doc.autoTable({ startY: 40, head: [['Metric', 'Value']], body: [ ['Test Type', 'Independent Samples T-Test'], ['Group 1 Sample Size (n1)', n1], ['Group 1 Mean', mean1.toFixed(3)], ['Group 2 Sample Size (n2)', n2], ['Group 2 Mean', mean2.toFixed(3)], ['T-Statistic', tStatistic.toFixed(3)], ['Degrees of Freedom', df], ['Conclusion', `Statistically ${isSignificant ? 'Significant' : 'Not Significant'} (p < 0.05)`] ] }); } else if (lastResults.type === 'Chi-Squared Test') { const { chiSquared, df, criticalChi, isSignificant } = lastResults.data; doc.autoTable({ startY: 40, head: [['Metric', 'Value']], body: [ ['Test Type', 'Chi-Squared Test of Independence'], ['Chi-Squared (χ²) Value', chiSquared.toFixed(3)], ['Degrees of Freedom', df], ['Conclusion', `Statistically ${isSignificant ? 'Significant' : 'Not Significant'} (p < 0.05)`] ] }); } doc.save('statistical-test-results.pdf'); }); });
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