So here is a question that gets asked quite often in our SPSS training courses - what exactly is the null hypothesis and how does it apply to may statistics.

Each time that we want to run a statistical test we need to formulate two hypotheses, and we then use the statistical test in SPSS to test between those two hypotheses. The null is the simplest explanation or hypothesis and the one that the researcher is trying to disprove . The alternative is usually the hypothesis that we want to test either for to prove or disprove depending on the circumstances.

Choosing an example to illustrate this. If we took a sample of 10 people at random from a larger population that we assumed that had equal numbers of men and women in it and found that 6 were female and 4 male, then the null hypothesis would be that there are equal numbers of men and women, whereas the alternative would be that there are more women than men in the population.

Having formulated our hypotheses we then run a test to test to test the hypotheses. Given that SPSS will know the distribution for the results of the test we can then calculate a p-value (significance) value for this data. This tells us how unlikely our result would be if the null hypothesis is true. If the p-value is small then we reject our null hypothesis.

The subject of p-values and their interpretation will be the subject of a further blog post later in the week. So check in later in the week to see the next in our series of questions that come up frequently during our SPSS training courses.

Click here for more details of Acuity Training's introductory SPSS training courses.

Each time that we want to run a statistical test we need to formulate two hypotheses, and we then use the statistical test in SPSS to test between those two hypotheses. The null is the simplest explanation or hypothesis and the one that the researcher is trying to disprove . The alternative is usually the hypothesis that we want to test either for to prove or disprove depending on the circumstances.

Choosing an example to illustrate this. If we took a sample of 10 people at random from a larger population that we assumed that had equal numbers of men and women in it and found that 6 were female and 4 male, then the null hypothesis would be that there are equal numbers of men and women, whereas the alternative would be that there are more women than men in the population.

Having formulated our hypotheses we then run a test to test to test the hypotheses. Given that SPSS will know the distribution for the results of the test we can then calculate a p-value (significance) value for this data. This tells us how unlikely our result would be if the null hypothesis is true. If the p-value is small then we reject our null hypothesis.

The subject of p-values and their interpretation will be the subject of a further blog post later in the week. So check in later in the week to see the next in our series of questions that come up frequently during our SPSS training courses.

Click here for more details of Acuity Training's introductory SPSS training courses.