What is a Critical Value?
A critical value defines regions in the sampling distribution of a test statistic. These values play a role in both hypothesis tests and confidence intervals. In hypothesis tests, critical values determine whether the results are statistically significant. For confidence intervals, they help calculate the upper and lower limits.
In both cases, critical values account for uncertainty in sample data you’re using to make inferences about a population. They answer the following questions:
- How different does the sample estimate need to be from the null hypothesis to be statistically significant?
- What is the margin of error (confidence interval) around the sample estimate of the population parameter?
In this post, I’ll show you how to find critical values, use them to determine statistical significance, and use them to construct confidence intervals. I also include a critical value calculator at the end of this article so you can apply what you learn.
Because most people start learning with the z-test and its test statistic, the z-score, I’ll use them for the examples throughout this post. However, I provide links with detailed information for other types of tests and sampling distributions.
Related posts: Sampling Distributions and Test Statistics
Using a Critical Value to Determine Statistical Significance
Critical values (CV) are the boundary between nonsignificant and significant results in a hypothesis test. Test statistics that exceed a critical value have a low probability of occurring if the null hypothesis is true. Therefore, when test statistics exceed these cutoffs, you can reject the null and conclude that the effect exists in the population. In other words, they define the rejection regions for the null hypothesis.
In this context, the sampling distribution of a test statistic defines the probability for ranges of values. The significance level (α) specifies the probability that corresponds with the critical value within the distribution. Let’s work through an example for a z-test.
The z-test uses the z test statistic. For this test, the z-distribution finds probabilities for ranges of z-scores under the assumption that the null hypothesis is true. For a z-test, the null z-score is zero, which is at the central peak of the sampling distribution. This sampling distribution centers on the null hypothesis value, and the critical values mark the minimum distance from the null hypothesis required for statistical significance.
Critical values depend on your significance level and whether you’re performing a one- or two-sided hypothesis. For these examples, I’ll use a significance level of 0.05. This value defines how improbable the test statistic must be to be significant.
Related posts: Significance Levels and P-values and Z-scores
Two-Sided Tests
Two-sided hypothesis tests have two rejection regions. Consequently, you’ll need two critical values that define them. Because there are two rejection regions, we must split our significance level in half. Each rejection region has a probability of α / 2, making the total likelihood for both areas equal the significance level.
The probability plot below displays the critical values and the rejection regions for a two-sided z-test with a significance level of 0.05. When the z-score is ≤ -1.96 or ≥ 1.96, it exceeds the cutoff, and your results are statistically significant.
One-Sided Tests
One-tailed tests have one rejection region and, hence, only one critical value. The total α probability goes into that one side. The probability plots below display these values for right- and left-sided z-tests. These tests can detect effects in only one direction.
Related post: Understanding One-Tailed and Two-Tailed Hypothesis Tests and Effects in Statistics
Using a Critical Value to Construct Confidence Intervals
Confidence intervals use the same critical values (CVs) as the corresponding hypothesis test. The confidence level equals 1 – the significance level. Consequently, the CVs for a significance level of 0.05 produce a confidence level of 1 – 0.05 = 0.95 or 95%.
For example, to calculate the 95% confidence interval for our two-tailed z-test with a significance level of 0.05, use the CVs of -1.96 and 1.96 that we found above.
To calculate the upper and lower limits of the interval, take the positive critical value and multiply it by the standard error of the mean. Then take the sample mean and add and subtract that product from it.
- Lower Limit = Sample Mean – (CV * Standard Error of the Mean)
- Upper Limit = Sample Mean + (CV * Standard Error of the Mean)
To learn more about confidence intervals and how to construct them, read my posts about Confidence Intervals and How Confidence Intervals Work.
Related post: Standard Error of the Mean
How to Find a Critical Value
Unfortunately, the formulas for finding critical values are very complex. Typically, you don’t calculate them by hand. For the examples in this article, I’ve used statistical software to find them. However, you can also use statistical tables.
To learn how to use these critical value tables, read my articles that contain the tables and information about using them. The process for finding them is similar for the various tests. Using these tables requires knowing the correct test statistic, the significance level, the number of tails, and, in most cases, the degrees of freedom.
The following articles provide the statistical tables, explain how to use them, and visually illustrate the results.
Related post: Degrees of Freedom
Critical Value Calculator
Another method for finding CVs is to use a critical value calculator, such as the one below. These calculators are handy for finding the answer, but they don’t provide the context for the results.
This calculator finds critical values for the sampling distributions of common test statistics.
For example, choose the following in the calculator:
- Z (standard normal)
- Two-tailed
- Significance level = 0.05
The calculator will display the same ±1.96 values we found earlier in this article.
Comments and Questions