Going to start off with some relevant definitions first Dependent variable — the thing that’s being measured, a response variable (ie. heart rate) Independent variable — the thing that’s being manipulated or set by the researchers; the variable that is hypothesized to cause a change in the dependent variable (ie. a drug treatment) Population — the group being tested. Important to note that the conclusion can only be generalized to the population of the study. If the experiment (“sample population”) only includes men of Asian descent aged 45 and over, then the conclusion cannot be assumed to extend to men of other backgrounds, women, young men, children, etc. The alternative hypothesis is the research question in the form of a true/false statement (This drug affects heart rate). The null hypothesis is the “blank.” It assumes there is no relation between the independent and dependent variables (This drug has no effect on heart rate). With no evidence, we default that the null hypothesis is true. The experiment aims to disprove the null hypothesis in favor of the alternative.* The p-value is the probability of getting the observed result under the assumption that the null hypothesis is true — that there is no relation between the variables. A small p-value means that it would be very unlikely to observe this result by random chance; therefore, it is likely that something is causing it. In a well-designed experiment, the cause can be attributed to the independent variable. *Just because a result is not significant, does not necessarily mean that the null hypothesis is definitively true, it just means we did not find evidence to say otherwise. Same goes for the alternative. Just because a result is significant, does not mean it is the end-all explanation. We just have evidence to support the conclusion. That’s not a go-ahead for all you conspiracy theorists out there to say “Gotcha!” If the results can be observed time and time again, then that’s more and more evidence to support the explanation. More on reddit.com

In data science, null generally refers to the null hypothesis. Let's say we're doing an experiment with two groups: Group A and Group B. These groups receive two different diets and lose varying amounts of weight. The null hypothesis is the statement "There is no difference in lost weight between Group A and Group B." So, if the average weight loss is 10 pounds for both groups, we can clearly say that the null hypothesis is true. Now, let's say that A loses 10 pounds and B loses 11 pounds. If we're being honest, that's not really Tha big a difference. It could have just been luck, right? That's why data scientists use a variety of statistical methods to compare groups. I won't go into too much depth, but basically the methods used answer the question: "Given Group A's data and Group B's data, how likely is it that the treatments actually had the same effect?" If you feel confident that the data is different enough, you can "reject the null hypothesis." That basically means that you have enough evidence to say that the null hypothesis is wrong. More on reddit.com

Imagine you are testing with low SES and access to healthcare resources (can't think of a better example rn). Your null hypothesis would be that low SES and access to healthcare are unrelated and there is no difference in access to healthcare with improving SES. Your alternative/ research hypothesis would be that there is a difference and the variables are related. More on reddit.com

The null is ALWAYS the opposite of what you want to prove. It is related to modus tollens. If A then B and Not B therefore not A. More on reddit.com