Critical Values Calculator

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Critical Values Calculator

Z-Distribution (Normal) T-Distribution (Student's) Chi-Square Distribution
Two-tailed Right-tailed (Upper) Left-tailed (Lower)
Critical Value
0.0000

What are Critical Values?

In statistics, a critical value is a point on the scale of the test statistic beyond which we reject the null hypothesis. It defines the boundary of the "rejection region"—the area of the distribution where the observed data is considered statistically significant.

Whether you are performing a Z-test, T-test, or Chi-Square test, the critical value depends on your chosen significance level (α) and the nature of your alternative hypothesis (one-tailed or two-tailed).

Commonly Used Critical Values

For a standard normal distribution (Z-distribution), the following values are frequently used:

Confidence Level Alpha (α) Z-Value (Two-tailed)
90% 0.10 1.645
95% 0.05 1.960
99% 0.01 2.576

How to Calculate Critical Values

1. Z-Distribution: Used when the population standard deviation is known or the sample size is large (n > 30). It follows the standard normal curve.

2. T-Distribution: Used when the population standard deviation is unknown and the sample size is small. It requires Degrees of Freedom (df), usually calculated as n – 1.

3. Chi-Square: Used for tests of independence or goodness-of-fit. These values are always non-negative and typically used for right-tailed tests.

Calculation Example

Suppose you are performing a two-tailed T-test with a 95% confidence level (α = 0.05) and a sample size of 15. Your degrees of freedom would be 14 (15 – 1). Using this calculator, you would select "T-Distribution", enter 0.05 for Alpha, 14 for Degrees of Freedom, and select "Two-tailed". The resulting critical value is ±2.1448.

function toggleDF() { var distType = document.getElementById("cvDistType").value; var dfGroup = document.getElementById("dfGroup"); if (distType === "t" || distType === "chi") { dfGroup.style.display = "flex"; } else { dfGroup.style.display = "none"; } } function rationalApprox(p) { // Abramowitz and Stegun formula for inverse normal distribution var c = [2.515517, 0.802853, 0.010328]; var d = [1.432788, 0.189269, 0.001308]; var t = Math.sqrt(-2 * Math.log(p)); return t – ((c[2] * t + c[1]) * t + c[0]) / (((d[2] * t + d[1]) * t + d[0]) * t + 1); } function getZScore(p) { if (p > 0.5) { return rationalApprox(1 – p); } else { return -rationalApprox(p); } } function calculateCV() { var distType = document.getElementById("cvDistType").value; var alpha = parseFloat(document.getElementById("cvAlpha").value); var tail = document.getElementById("cvTail").value; var df = parseFloat(document.getElementById("cvDF").value); var resultLabel = document.getElementById("cvResultLabel"); var resultValue = document.getElementById("cvResultValue"); var resultBox = document.getElementById("cvResultBox"); if (isNaN(alpha) || alpha = 1) { alert("Please enter a valid alpha between 0 and 1."); return; } var p; var cv; if (distType === "z") { if (tail === "two") { p = alpha / 2; cv = Math.abs(getZScore(p)); resultValue.innerHTML = "± " + cv.toFixed(4); resultLabel.innerHTML = "Critical Z-Value (Two-tailed)"; } else if (tail === "right") { p = alpha; cv = Math.abs(getZScore(p)); resultValue.innerHTML = cv.toFixed(4); resultLabel.innerHTML = "Critical Z-Value (Right-tailed)"; } else { p = alpha; cv = -Math.abs(getZScore(p)); resultValue.innerHTML = cv.toFixed(4); resultLabel.innerHTML = "Critical Z-Value (Left-tailed)"; } } else if (distType === "t") { if (isNaN(df) || df = 1)."); return; } // T-distribution approximation (Hill's formula) var z; if (tail === "two") { z = Math.abs(getZScore(alpha / 2)); } else { z = Math.abs(getZScore(alpha)); } // Cornish-Fisher expansion for T var t = z + (Math.pow(z, 3) + z) / (4 * df) + (5 * Math.pow(z, 5) + 16 * Math.pow(z, 3) + 3 * z) / (96 * Math.pow(df, 2)); if (tail === "two") { resultValue.innerHTML = "± " + t.toFixed(4); resultLabel.innerHTML = "Critical T-Value (Two-tailed)"; } else if (tail === "right") { resultValue.innerHTML = t.toFixed(4); resultLabel.innerHTML = "Critical T-Value (Right-tailed)"; } else { resultValue.innerHTML = "-" + t.toFixed(4); resultLabel.innerHTML = "Critical T-Value (Left-tailed)"; } } else if (distType === "chi") { if (isNaN(df) || df = 1)."); return; } // Wilson-Hilferty transformation for Chi-Square var zScore; if (tail === "two") { zScore = getZScore(alpha / 2); // Approximation for upper tail } else if (tail === "right") { zScore = getZScore(alpha); } else { zScore = getZScore(1 – alpha); } // Formula: df * (1 – 2/(9*df) + z*sqrt(2/(9*df)))^3 var factor = 2 / (9 * df); var base = 1 – factor + Math.abs(zScore) * Math.sqrt(factor); cv = df * Math.pow(base, 3); resultValue.innerHTML = cv.toFixed(4); resultLabel.innerHTML = "Critical Chi-Square Value"; } resultBox.style.display = "block"; }

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