Hypothesis testing: χ² and t-tests

Maths: Applications and Interpretation · Statistics and probability. The idea in plain words, the rules to remember, the mistake to avoid and a worked example, written and checked by Mega Lecture.

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The idea

A hypothesis test asks whether data give enough evidence against a starting claim. The null hypothesis H0 says nothing special is happening: the variables are independent, the data fit the model, or the two means are equal. The alternative hypothesis H1 says the opposite.

The calculator gives a p-value: the probability of data this extreme if H0 is true. A small p-value is evidence against H0.

Use a χ2 test for frequencies (counts in categories) and a t-test to compare the means of two groups. The χ2 test compares the observed frequencies with the expected frequencies you would get if H0 were true.

Rules to remember

Common mistake

Students reverse the decision. A p-value BELOW the significance level means reject H0. And in a χ2 test for independence H0 is always that the variables are independent, so rejecting it means there is evidence that they are related.

Worked example

A 2 by 3 contingency table has grand total 150. One cell has row total 60 and column total 45. Find its expected frequency and the degrees of freedom. If χ2calc = 4.10 and the 5% critical value is 5.991, what is the conclusion?

  1. Expected frequency = (60 × 45) ÷ 150 = 2700 ÷ 150 = 18
  2. Degrees of freedom = (2 − 1) × (3 − 1) = 1 × 2 = 2
  3. Compare: 4.10 < 5.991, so χ2calc is below the critical value

Answer: Expected frequency 18, 2 degrees of freedom; do not reject H0, so there is not enough evidence that the variables are related.

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