Critical Values in Statistics: A Beginner's Guide

Critical Values in Statistics: A Beginner's Guide

 In statistics, critical values play an important role in hypothesis testing, confidence intervals, and making informed decisions based on data analysis. These values help determine whether a test statistic falls within a specific range, enabling us to assess the significance of results and draw meaningful conclusions.

The fundamentals of critical value, including its definition, formulas, t-test critical value, and advanced concepts have been covered in this article.  A more thorough example of critical value is discussed in this article.

What is Critical Value?

A critical value (C.V) is a boundary point that defines a range of values for the test value that indicates that indicate that the null hypothesis should be rejected since there is a substantial difference.Critical values are fundamental in statistical analysis, serving as thresholds that help determine the significance of results and guide decision-making.

In hypothesis testing, confidence intervals, and other statistical procedures, these values mark the boundary between accepting or rejecting a hypothesis or determining the range within which a parameter lies. By comparing calculated test statistics with critical values, statisticians gain insights into the reliability of their conclusions, ensuring robust and informed interpretations of data.

Formulas of critical value

Here, we discuss the sum critical formula that we used for finding the critical value.

Critical value for t-test

The following formula can be used to determine the t critical value:

·         Determine the alpha level.

·         Using the degree of freedom, reduce the size of sample by one.

·         One-tailed distribution is used only in those cases when the test hypothesis test is one-tailed. In all other cases, we utilize a two-tailed t-distribution table.

·         Match the top row's alpha value with the equivalent df value on the table's left side. To get the t crucial value, locate the junction of this row and column.

Formula for the one-tailed t-test

t = x-μ/ (s/√n).x


·         μ = population

·         n = size of the sample

·         s = sample standard deviation 

Formula: For two-tailed t-test

t=(x1-x2)-(μ12)/√(s21/n1 + s22/n2)

Advanced Concepts of Critical Value

Critical values, while foundational in statistics, hold deeper nuances that become apparent when delving into more advanced concepts and specialized applications. Here, we delve into some of these advanced aspects of critical values:

·         Non-Parametric Tests:

In addition to the traditional parametric tests that assume specific population distributions, non-parametric tests provide alternatives that are distribution-free. Advanced critical values are designed for these tests, which include the Wilcoxon rank-sum test, Kruskal-Walli’s test, and the Mann-Whitney U test.

·         Multivariate Analysis:

In multivariate statistics, analyzing data with multiple variables, critical values extend to matrices and higher-dimensional spaces. Advanced techniques like Hotelling's T-squared test for comparing means in multivariate data require specialized critical values that consider the interactions between variables.

·         Multiple Critical Values:

In some scenarios, multiple critical values are involved. For example, in ANOVA (Analysis of Variance), there can be multiple F-critical values corresponding to different degrees of freedom. Understanding which critical value to use in different situations is crucial.

·         Correlation and Regression Analysis:

In correlation and regression analyses, critical values are used to determine the statistical significance of coefficients and relationships between variables. The advanced interpretation lies in considering the strength and direction of relationships in addition to just significance.

·         Large-Sample Approximations:

For large samples, normal distribution approximations are often used, allowing z-critical values to be used instead of t-critical values. Advanced techniques involve determining when this approximation is valid and when t-distribution critical values should still be employed.

·         Bayesian Inference:

In Bayesian statistics, which involves incorporating prior information into analyses, critical values take on a different interpretation. They play a role in constructing credible intervals, which are the Bayesian analog of confidence intervals.

·         Advanced Hypothesis Testing:

Advanced hypothesis testing scenarios, such as one-sample or two-sample tests for proportions, require tailored critical values. Understanding the underlying distributions and the nuances of each scenario is essential.

·         High-Dimensional Data:

With the rise of big data and high-dimensional datasets, critical value calculations become more complex. Advanced methods involve adjusting critical values for multiple testing to control for false discoveries.

·         Time Series Analysis:

In time series analysis, where data points are observed sequentially over time, advanced critical values account for temporal dependencies and autocorrelation.

·         Machine Learning and AI:

As machine learning and artificial intelligence become prominent, critical values find application in assessing model performance and statistical significance in a dynamic, data-driven environment.

How to find the critical value?

Below are a few solved examples to learn how to find critical value.

Example 1:

Sample size= 10

Significance level = 0.025

t critical value=?

Step 1:

To find the df=?

df= n-1

df=10-1= 9


Step 2:

It depends on the test, so, we chose the one-tailed or two-tailed distribution table

Step 3:

Now to find the t critical value we use the t- table we see the value of α from the top row and df from the left column.


T-critical value= 2.093

Example 2:

Assume that the t-test sample size is 5, and that =0.025. and the sample's population mean 13 and 20 individually. The sample's S.D. is 25. Find the t-value.


Step 1:Givan data

Mean of sample = 13,                         mean of population = 20

S.D = 25,                                                  Size = 5


Step 3:Putting value in t formula

x̄ = 13, μ =20, s = 25, n = 5

Putting value in t formula

t- value= [(25 - 20)]/ [(25/ √5)]

= [(5)] / [(25/ 2.23)]

= [5] / [11.21]

ð   0.4460


Question number 1:

What determines the choice of critical values?


The choice of critical values depends on the desired level of significance (alpha), the distribution being used (e.g., normal or t-distribution), the degrees of freedom, and whether the test is one-tailed or two-tailed.

Question number 2

Are critical values the same for all significance levels?


No, critical values vary based on the chosen significance level. Higher significance levels result in more extreme critical values, making it harder to reject the null hypothesis.

Question number 3:

How do you find critical values from a table?


Critical values can be found in statistical tables specific to the chosen distribution and significance level. Locate the degrees of freedom and the corresponding significance level to identify the critical value.


The fundamentals of critical value, including its definition, formulas, t-test critical value, and advanced concepts have been covered in this article.  A more thorough example of critical value is discussed in this article. Anyone can defend easily after completely understanding this article.