Research what does p mean

Or could it be that Group A lost more weight simply by chance? Statistical testing starts off by assuming something impossible: that the two groups of people were exactly alike from the start. This means the average starting weight in each group was the same, and so were the proportions of lighter and heavier people. Mathematical procedures are then used to examine differences in outcomes weight loss between the groups.

The goal is to determine how likely it is that the observed difference — in this case, the three-kg difference in average weight loss — might have occurred by chance alone. Now here's where it gets complicated.

Scientists use the term "p" to describe the probability of observing such a large difference purely by chance in two groups of exactly-the-same people. In scientific studies, this is known as the "p-value. If it is unlikely enough that the difference in outcomes occurred by chance alone, the difference is pronounced "statistically significant. Mathematical probabilities like p-values range from 0 no chance to 1 absolute certainty.

In most sciences, results yielding a p-value of. It is a statement about data in relation to a specified hypothetical explanation, and is not a statement about the validity of the explanation itself.

A P-value of 0. The P-value tells you nothing more than this. Any further inference about whether study findings should be accepted or rejected should be based on evaluation of the study design, results of other statistical tests, the plausibility of the clinical question and outcomes, and evaluation of the strengths and limitations of a study.

The ASA statement on P-values is available here. Hello I would like to point out that although this article is seeking to reconcile the correct understanding of a p-value, with two statements it is actually perpetuating misunderstanding:.

Quote: A P-value of 0. According to the literature these two statements again perpetuate common misuses of the p-value: 1. When should we draw conclusions from the differences in the data? This blog series aims to demystify some of the common questions we get on statistics. In this blog post we will be talking about what a p-value actually is, why does it have to be less than 0.

A great way of understanding your data is to run an appropriate statistical test. You could set up a research study where participants complete a task on each prototype, for one of your key user journeys. One question you can ask participants after they complete a task is to rate the ease or difficulty of using the prototypes.

As a UserZoom client, you can look in the Study Results and in this instance see Prototype A has an average ease-of-use rating of 5. In our example, we assume a rating of 1 reflects hard to use, and 7 reflects easy to use. Prototype B therefore has the higher mean, so participants rated it, on average, easier to use. But does a higher mean rating for Prototype B actually represent anything?

How big does a difference need to be? Here, the difference between the means the mean difference is 0. Are we prepared to dismiss Prototype A just because on average it was rated 0. Rather than just relying on your data to only show you descriptive information e. In this scenario, is one of the prototypes going to be easier to use than the other? There are many different statistical tests out there, and the one you use depends on the design of your study and the type of data you have collected.

One thing they have in common is the p-value. The p-value helps us to decide if we have strong enough evidence to make conclusions about our data. In the context of our difficulty ratings, we would be testing whether the difference in our means 0. The p-value we obtain from running a statistical test on this data is the probability of obtaining this mean difference if we assume that there is no difference between our prototypes to find.

In the case of our ease of use means, the p-value tells us the probability of obtaining the difference between our means if there truly was no difference between the prototypes.

As with other statistical tests, what we require is enough evidence to be happy to conclude that our mean difference is there. If we obtain a p-value of less than 0. By setting the p-value at 0.

If the p-value we obtain is less than 0. In contrast, if our statistical test came back with a p-value of 0. If we set the p-value to be 0.