Interpreting Your Data Analysis: How to Determine Statistical Significance

Quantitative Results

Conducting your data analysis and drafting your results chapter are important milestones to reach in your dissertation process. The light is finally shining on you from the end of the tunnel, and you are winding down. With only two chapters to go, you are finally feeling relieved… until you get the output from your data analysis. What do these numbers mean and what should you do with them?

With p values, t values, F values, correlation coefficients, and a bunch of other numbers staring at you, it is easy to get discouraged. However, the basic question you need to answer, do I or do I not have statistical significance, can be answered looking at one simple number: the p value.

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Before you can determine if you have rejected or failed to reject your null hypothesis, you must designate the maximum probability of falsely rejecting the null hypothesis that you are willing to accept in your analysis. This is referred to as the alpha level and is typically set at .05 in social science research. An alpha level of .05 means that you are willing to accept up to a 5% chance of rejecting the null hypothesis when the null hypothesis is actually true. Depending on your field of study and the nature of your analysis, you may choose to decrease or increase the alpha level to make the decision point more or less stringent.

Once you conduct your analysis, you will get a p value, also called a significance (Sig.) value. Your statistical software package will return this number to you once you conduct your analysis. This number reflects the probability of obtaining results as extreme as what you obtained in your sample if the null hypothesis was true.

Let’s give this concept some legs with an example. Our research question asks: are there differences in the number of women hired in higher education institutions by region? The null hypothesis we are testing is: there are no differences in the number of women hired by region. We conducted a one-way ANOVA in order to compare the number of women hired in each region. The alpha level for this analysis is .05. We conducted the analysis in SPSS and got the following output:



Sum of Squares


Mean Square



Between Groups






Within Groups








We have a p value (Sig.) of .375. This number exceeds our alpha level of .05. Therefore, we fail to reject the null hypothesis. We did not find statistically significant differences in the number of women hired in higher education institutions by region. If the p value had been less than .05, we would have rejected the null hypothesis.