If you are going to implement a quantitative design for your thesis or dissertation, you will probably be using some form of null hypothesis significance testing. It may have been a while since you took your graduate-level statistics course, so the following is a brief refresher about what a null hypothesis is.

Null Hypothesis Significance Testing

In most quantitative research questions, there are both null hypotheses (noted as H0) and alternative hypotheses (noted as H1). In their most simplistic state, null hypotheses note that there will be no difference/effect/relationship (the term you use depends on the type of statistics you run) between key study variables. The opposite of a null hypothesis is an alternative hypothesis, which is basically what you learned about hypotheses in science class: propositions about what you expect will happen in your study in regards to your research question(s).

Null hypotheses significance testing is the probability that a relationship resulted from the chance of random sampling. Typically in research, the significance level (a.k.a. alpha level) is .05; you may recall seeing p < .05 in your early coursework in statistics. When we use an alpha level of .05, we as researchers are willing to accept a 5% chance that the findings are due to chance. If the test statistic that you obtained is greater than the critical value (which is the value associated with the alpha level of .05), then you would reject the null hypotheses, which is essentially saying that the alternative hypotheses appear to be true. If the test statistic that you obtained is less than the critical value, you cannot reject the null hypotheses.

Types of Errors


There are two types of errors that researchers can make when using null hypotheses significance testing: Type I and Type II errors. Type I errors (also referred to as α errors) occur when researchers reject null hypotheses when in fact the null hypotheses are true. Type II errors (also referred to as β errors) occur when researchers fail to reject null hypotheses when the null hypotheses are false.


Steps in Null Hypothesis Significance Testing

Below are the five steps to null hypotheses significance testing. It is important for you to know and understand these steps, but there are many statistical software programs that will actually do most of this for you when you run the appropriate statistic.

1. State your hypotheses (both the null and the alternative).

2. Select your desired significance level (typically .05).

3. Calculate the test statistic.

4. Find the critical value.

5. Make your decision about null hypotheses using the results of the test to determine if the obtained statistic is greater or less than the critical value.


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