More or less, the null hypothesis is a hypothesis that states there wasn't anything important discovered in observation. If it's a two-group trial and control study, the null hypothesis is generally "the trial group is no different".

If the study is testing a medication, the null hypothesis is "it doesn't do anything".

If the study is comparing gender differences in some mental task, the null hypothesis is "there isn't a difference".

More on reddit.com

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

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

I.e the statistic is the same as the parameter. You should start by rethinking this statement. Statistics are variables, calculated from data. Sample statistics are individual realizations of the variable, but they are basically the parameter +/- uncertainty, so we never actually think the statistic is the same as the parameter. why do we include inequalities in the Null Hypothesis This is generally only the case in 1-sided hypothesis testing, not in general. But it basically boils down to how we choose a (1-alpha)% region of the null distribution to define as our critical region / rejection region. If we choose rejection regions from both tails of the null distribution, we have a two-sided hypothesis test, since we are allowing rejections of the null in both directions. If we pull our entire rejection region from only one tail, we maximize our statistical power in that direction, but then we are incapable of rejecting the null at all in the other direction, so we basically incorporate that direction into the null hypothesis statement itself - it's honestly a bastardization of notation, but it does convey the fact that we are only interested in 1 side of the null distribution. I'm not sure at what level you understand these things, so feel free to ask follow-up questions. 2) What purpose does an alternative hypothesis serve? Once we reject the null, the alternative hypothesis is basically the "alternative" to the null that we accept as "likely being true" (maybe a bit of a simplification, but that's the gist). More on reddit.com