Valor crítico t

t_(α,df) para α = 0.05, 0.01, 0.10.
Created by
Renato Passos, Eng. de Software
Reviewed by
Renato Passos, Eng. de Software

Last updated: Apr 18, 2026

t crítico
2,0751

Formula

t = t_(α/2, df)

About this calculator

The critical t-value is a fundamental concept in statistics, particularly in hypothesis testing and confidence intervals. It is used to determine the rejection region in a Student's t-test. The critical t-value calculator provides the value of t_(α, df) for different significance levels (α) and degrees of freedom (df).

The formula used is based on the Student's t-distribution, which is symmetric around the mean. The critical t-value is calculated based on the probability α and the degrees of freedom df. This allows users to determine if a calculated t-statistic from a sample is significantly different from an expected value.

Critical t-values are commonly used for α = 0.05, 0.01, and 0.10, which correspond to confidence levels of 95%, 99%, and 90%, respectively. The choice of α depends on the context and the desired precision. The degrees of freedom df are also crucial, as they affect the shape of the t-distribution.

When using the critical t-value calculator, it is essential to be cautious with the interpretation of the results. Critical t-values should be compared with the t-statistic calculated from the sample data. If the t-statistic is more extreme than the critical t-value, the null hypothesis may be rejected.

Frequently asked questions

What is the critical t-value and how is it used?

The critical t-value is a threshold value used in hypothesis testing to determine if a calculated t-statistic is significantly different from an expected value.

What are the most common significance levels used for the critical t-value?

The most common significance levels are α = 0.05, 0.01, and 0.10, corresponding to confidence levels of 95%, 99%, and 90%, respectively.

How do degrees of freedom affect the critical t-value?

Degrees of freedom affect the shape of the t-distribution and, therefore, the critical t-value. The higher the degrees of freedom, the closer the t-distribution will be to the normal distribution.

What happens if the calculated t-statistic is more extreme than the critical t-value?

If the calculated t-statistic is more extreme than the critical t-value, the null hypothesis may be rejected, suggesting a significant difference.

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