Understanding Critical Values: An Introduction with Examples

In statistical analysis, critical values are crucial tools that aid researchers and analysts in selecting appropriate hypotheses and determining the importance of their findings. They offer a definite cutoff for deciding whether the observed data is statistically significant or if random fluctuation can be used to explain it.

Understanding Critical Values: An Introduction with Examples

In this article, we’ll explore the introduction of critical value, factors affecting critical value, and applications of critical values. 

Critical Values

In statistics, a critical value plays a crucial role in hypothesis testing and determining the significance of results. It is a predefined threshold or specific numerical value used to make decisions about a statistical test, such as a hypothesis test or confidence interval calculation. 

The concept of critical values is fundamental in determining whether an observed statistic is statistically significant or falls within the range of expected variation.

Affecting Factors of Critical Value 

Several factors can affect the critical value used in statistical hypothesis testing.

Significance Level (α): 

The significance level, often denoted as α (alpha), represents the Probability of making a Type I mistake, which involves wrongly ruling out a valid null hypothesis. Commonly used significance levels include 0.05 (5%) and 0.01 (1%). As the significance level decreases, critical values increase, making it more challenging to reject the null hypothesis.

Type of Test: 

When a statistical test is being performed the types of critical value play an important role. Different type of tests like, such as t-tests, z-tests, chi-squared tests, and F-tests, have their critical value tables or formulas based on the specific probability distribution relevant to that test.

Degrees of Freedom (df): 

In critical value, df plays an important role in the finding process. Both the number of forms of critical value and the degree of freedom are connected. 

Two-Tailed or One-Tailed Types of Hypothesis: 

Hypothesis tests nature’s effect on the critical value. In a one-tailed test, critical values are typically placed in only one tail of the distribution, depending on whether you are testing for a specific direction of deviation. In a two-tailed test, where you are interested in deviations from the null hypothesis in both directions of the distribution, critical values are placed in both tails. 

Sample Size: 

For certain tests, especially those based on the normal distribution, larger sample sizes can lead to smaller critical values. This is because larger samples tend to have less sampling variability and more closely approximate the population distribution.

Desired Confidence Level: 

In confidence interval estimation, the critical value is associated with the desired confidence level. Higher confidence levels require larger critical values to account for a wider range of possible values in the confidence interval.

Examples of Critical Value

Example #1:

Let’s suppose the level of significance is 8% and the degree of freedom is 40 and determine the t value.


Step 1:

identify the value given data 

α = 8% = 8/100 = 0.08

df = 40 

Step 2:

By using the table of critical value 

One-Tailed Probability of 0.08

df α df α df α df α 

Try a t table calculator by a Calculator to get the result of t critical value according to the t-distribution table in few seconds.

Example 2:

Let’s suppose the significance level is 0.10% and degree of freedom of the nominator is 10 and the degree of freedom of the denominator is 15 determines the F critical value.


Step 1:

Given value 

α = 0.10% 

df (nominator)= 10

df (denominator) = 15

Step 2:

By using the F critical calculator value of critical is 

Critical value = 2.059

Application of critical value

Goodness-of-Fit Tests: Critical values are used in goodness-of-fit tests, such as the chi-squared test, to assess whether observed data fits a particular probability distribution. By comparing the calculated test statistic to the critical value, you can determine whether the fit is statistically significant.

ANOVA (Analysis of Variance): In ANOVA, critical values are employed to evaluate the differences in means among multiple groups or treatments. The F-statistic is compared to a critical value to determine if there are significant differences among group means.

Process Control: In quality control and Six Sigma methodologies, critical values are utilized to assess whether a manufacturing or production process is in control or out of control. Control charts and statistical process control techniques often rely on critical values to make these judgments.

Risk Management: Critical values are important in risk analysis and decision-making. They help identify extreme values or outliers in data that could indicate unusual or high-risk situations.


In this article, we have discussed the introduction of critical value, factors affecting critical value, and the application of critical value. Furthermore, with the help of examples critical value is more explained for a better understanding of the reader. Anyone can defend this topic easily after studying this article.


Question number. 1:

How are critical values determined?


Critical values are determined based on the probability distribution associated with the statistical test being performed. Tables, calculators, software, or mathematical formulas are used to find critical values.

Question number. 2:

Are critical values used in real-world applications outside of statistics?


Yes, critical values are used in various fields, including quality control, environmental monitoring, risk analysis, and medical research, to assess significance and make informed decisions.

Question number. 3:

How do I find the critical value for my statistical test?


You can find critical values in statistical tables, use statistical software or calculators, or refer to relevant textbooks and resources specific to your test.

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