Chi-Square test for One Pop. Variance


Instructions: This calculator conducts a Chi-Square test for one population variance (\(\sigma^2\)). Please select the null and alternative hypotheses, type the hypothesized variance, the significance level, the sample variance, and the sample size, and the results of the Chi-Square test will be presented for you:

Ho: \(\sigma^2\) \(\sigma_0^2\)
Ha: \(\sigma^2\) \(\sigma_0^2\)
Hypothesized Variance (\(\sigma_0^2\))
Sample Variance (\(s^2\))
Sample Size (n)
Significance Level (\(\alpha\))



Chi-Square test for One Population Variance

More about the Chi-Square test for one variance so you can better understand the results provided by this solver: A Chi-Square test for one population variance is a hypothesis that attempts to make a claim about the population variance (\(\sigma^2\)) based on sample information.

The test, as every other well formed hypothesis test, has two non-overlaping hypotheses, the null and the alternative hypothesis. The null hypothesis is a statement about the population variance which represents the assumption of no effect, and the alternative hypothesis is the complementary hypothesis to the null hypothesis. The main properties of a one sample Chi-Square test for one population variance are:

  • The distribution of the test statistic is the Chi-Square distribution, with n-1 degrees of freedom

  • The Chi-Square distribution is one of the most important distributions in statistics, together with the normal distribution and the F-distribution

  • Depending on our knowledge about the “no effect” situation, the Chi-Square test can be two-tailed, left-tailed or right-tailed

  • The main principle of hypothesis testing is that the null hypothesis is rejected if the test statistic obtained is sufficiently unlikely under the assumption that the null hypothesis is true

  • The p-value is the probability of obtaining sample results as extreme or more extreme than the sample results obtained, under the assumption that the null hypothesis is true

  • In a hypothesis tests there are two types of errors. Type I error occurs when we reject a true null hypothesis, and the Type II error occurs when we fail to reject a false null hypothesis

The formula for a Chi-Square statistic is

\[\chi^2 = \frac{(n-1)s^2}{\sigma^2}\]

The null hypothesis is rejected when the Chi-Square statistic lies on the rejection region, which is determined by the significance level (\(\alpha\)) and the type of tail (two-tailed, left-tailed or right-tailed).




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Chi-Square test for One Pop. Variance

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