Choose your method
Exploring different statistical tests
There are several different statistical tests that you can use, depending on the type of data you are dealing with. Two examples are given here. You might also want to have a look at our worked-through example for chi-squared (‘chi’ is pronounced ‘ki’ to rhyme with ‘eye’).
The chi-squared test is used with categorical data to see whether any diﬀerence in frequencies between your sets of results is due to chance. For example, you could use the test with the null hypothesis that ‘there is no diﬀerence in the frequency of worms on different types of ground’.
In a chi-squared test, you draw a table of your observed frequencies and your predicted frequencies and calculate the chi-squared value. You compare this to the critical value to see whether the diﬀerence between them is likely to have occurred by chance. If your calculated value is bigger than the critical value, you reject your null hypothesis.
The t-test enables you to see whether two samples are different when you have data that are continuous and normally distributed. The test allows you to compare the means and standard deviations of the two groups to see whether there is a statistically signiﬁcant diﬀerence between them. For example, you could test the heights of the members of two different biology classes.