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You have probably heard scientists quote p-values whenever they report the results from their experiment. But what exactly is a p-value anyway, in this video? I would clearly explain what a p-value is. A p-value is an abbreviation for probability value and the p-value is a number that can be any value between 0 and 1 But what exactly does this number represent? The official definition of a p-value is quite difficult to understand a p-value is the probability of obtaining the observed difference or a larger one in the outcome measure, given that no difference exists between treatments in the population. So the best way to explain what P-value is is to use an example. Let’s say you want to perform an experiment to see if a new type of weight-los’s drug drug X causes people to lose weight, so you randomly sample a collection of von tears and randomly assign them into two groups Group A and Group B by the way. If you don’t know the difference between a sample and a population, it might be worth checking out the previous video you give. Group A a placebo. In other words. This contains no active ingredients. Group A are therefore the control group and you give Group B The new drug drug X. The participants are weighed at the start of the study and at the end of the study. And this way, you can work out the body weight difference at the end of the study. You work out the group? As average body weight difference with zero kilograms, in other words, they did not gain or lose any body weight group. B’s body weight difference was negative, one kilogram, so an average. They lost one kilogram of their body weight. So does this mean that the drug worked to determine this? We first asked ourselves what would happen in a world where the weight difference in volunteers who received drug acts is the same as the weight difference who received the placebo. This is where the null hypothesis comes in. Usually the null hypothesis States that there are no difference between groups, for example, So our null hypothesis. Is that the weight difference in those who receive drug? X is the same as the weight difference in those who receive the placebo now. We can ask ourselves if this null hypothesis we’re true, what is the chance or probability of discovering a one-kilogram reduction or more in body weights in those treated with drug acts from our sample, this probability or p-value measures the strength of evidence against the null hypothesis, and you can think of this as a court trial where the defendant is innocent and so proven guilty in this case? The defendant is the null hypothesis, The smaller, the p-value the stronger the evidence against the null hypothesis to determine the p-value scientists use what are known as statistical hypothesis tests. Common examples include the student t-test and a one-way. Anova since this is a top-line overview, I will not bombard you with statistical jargon, but instead pretend we have performed a statistical test using our data so after inputting our data into a statistical test, we get a p-value in return. Let’s say, For example, the p-value is 0.02 it’s worth mentioning that the p-value is a fraction. However, it may be easier to convert this to a percentage to simply understand the concept better, so a value of zero point zero two would be two percent. I simply multiplied the fraction by 100 But what does this p-value resort of 0.02 or 2% actually represent? Essentially, This means that if the null hypothesis were true, in other words that the two population means are identical, then there is a 2% chance of observing a difference as large or larger than what we observed in our sample. In our example, this would translate to in a world where the weight difference in those who receive drug. X is the same as the weight difference in those who receive the placebo. Then there is a 2% chance of observing a weight loss of 1 kilogram or more between our sample groups to put that into perspective. A 2% chance is one in every 50 experiments, but how can this be? What is accounting for this 2% Simply, this 2% can be accounted for by random noise. Let’s elaborate a bit more on random noise. There are quite a few things that can impact the p-value and some of these factors are collectively known as random noise or random chance, one type of factor that can contribute to random noise, especially in human studies. Is the coincidence of random sampling? For example, humans can exhibit a large amount of variation between one another due to genetic and environmental influences. If we relate back to our example, some humans may contain an unknown gene that speeds up their metabolism and causes them to lose weight more than those without the gene. When recruiting volunteers for our experiment, we did not perform any DNA analysis before randomly assigning the volunteers to either Group a the control group or Group B the drug X group, So there was no way of knowing who was a carrier of this gene or not, so imagine a situation where, just by pure coincidence, More volunteers with the high metabolism gene, a placed in Group B compared with Group. A so you can see that this scenario favors group B. Ultimately, you can see that just by pure coincidence of random sampling, this can have a knock-on effect on the p-value so to sum up a p-value is a value between 0 & 1 This p-value represents the probability of obtaining the observed difference or a larger one in the outcome measure of the sample, given that no difference exists between the treatments in the population. In other words, when the null hypothesis is true and finally, random noise can affect the p-value. A common example of random noise is a coincidence of random sampling. 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