Hypothesis Testing for Means & Proportions
Hypothesis Testing - Analysis of Variance (ANOVA)
If the absolute value of the t-value is greater than the critical value, you reject the null hypothesis. If the absolute value of the t-value is less than the critical value, you fail to reject the null hypothesis. You can calculate the critical value in Minitab or find the critical value from a t-distribution table in most statistics books. For more information calculating the critical value in Minitab, go to and click Use the ICDF to calculate critical values.
The techniques for hypothesis testing depend on
In the previous example, we set up a hypothesis to test whether a sample mean was close to a population mean or desired value for some soil samples containing arsenic. On this page, we establish the statistical test to determine whether the difference between the sample mean and the population mean is significant. It is called the t-test, and it is used when comparing sample means, when only the sample standard deviation is known.
For example, the alternative hypothesis is
In statistics, if you want to draw conclusions about a null hypothesis H0 (reject or fail to reject) based on avalue, you need to set a predetermined cutoff point where only those -values less than or equal to the cutoff will result in rejecting H0.
In an applied hypothesis testing
If H0 is rejected (that is, the -value is less than or equal to the predetermined significance level), the researcher can say she’s found a statistically significant result. A result is if it’s too unlikely to have occurred by chance assuming H0 is true. If you get a statistically significant result, you have enough evidence to reject the claim, H0, and conclude that something different or new is in effect (that is, Ha).