# Can I use p-values without hypothesis?

I have been working on my Master's Thesis which included a survey and its results have been put through various tests. The unusual thing about my Thesis is that I have no hypotheses at all, making it a descriptive statistical analysis. However, I have set up hypotheses just for the purpose of calculating p-values for measuring the significance threshold (at 1%, 5% and 10%). In the meantime, my Mentor reminded me of the lack of hypotheses emphasizing the fact that I should not have any.

My question is, can I use the information without using the hypotheses as they are, meaning does the information of the significance threshold have any value without the hypotheses and could I rephrase them so that they look like "claims" rather than hypotheses? I would hate for all this work and information to go to waste but if it's impossible to use it I cannot do much about it.

• Commented Dec 6, 2023 at 13:48
• None of your work went to waste. All the other output can be used: Parameter estimates, standard errors, etc. are all still usable. Commented Dec 6, 2023 at 13:50
• You can turn the p-value calculations into a confidence interval.en.wikipedia.org/wiki/Confidence_interval Commented Dec 6, 2023 at 14:14
• This confidence interval seems intriguing, might check that out! Thanks all!
– Mel
Commented Dec 6, 2023 at 14:35
• But why oughtn't you to have any hypotheses? If, for example, as is hinted at by your use of 'descriptive', inference on population parameters is being ruled out for some reason, you oughtn't to have any point or interval estimates either. Commented Dec 7, 2023 at 2:23

You can't even calculate p values without a null hypothesis, because this is a crucial ingredient that goes into all tests, along with data and a model specification.

For instance, for a t test for a regression coefficient, you typically test against the null hypothesis that $$b=0$$, and this is frequently not spelled out. But you can certainly test against other null hypotheses.

So the question really is whether these "default" hypotheses make sense in your case. This is something you could discuss with your advisor. Very often, we know that this kind of null hypothesis is trivially or at least very highly likely wrong before even looking at the data.

• We were typing simultaneously. I'm glad our answers agree! Commented Dec 6, 2023 at 13:43
• I will probably delete the whole test since it's not applicable in my case. Do you have any suggestions on what other tests I could use (apart from normality and Cronbach's alpha since I already did them) that are typical for a descriptive statistical analysis?
– Mel
Commented Dec 6, 2023 at 14:42
– Mel
Commented Dec 6, 2023 at 14:42
• @PeterFlom: great minds type alike. Commented Dec 6, 2023 at 15:19
• @Mel: if you are mainly interested in descriptive and exploratory analyses, then there are lots of descriptive statistics you could report, from the mean and the SD to ranges. However, I would always try to visualize as much as possible - a violin or beanplot of your raw data is much more informative than the mean and the SD. There are many possible visualizations. Which one is best depends on your situation, your data and what is interesting about both. Good luck! Commented Dec 6, 2023 at 15:21

Why do you even want a p value when you don't have a hypothesis?

But, in short, the answer is "no". I mean, you can't stop the computer from outputting a p value, but, every p value has an inherent null hypothesis, usually that something isn't happening (e.g. that $$\beta = 0$$ or that $$\mu = 0$$ or that the OR is 1).