Take a look at this book from the z-library.

David Booth

A hypothesis is a hypothetical explanation for a group of facts that can be tested by further inquiry. The null hypothesis and the alternative hypothesis are the two main categories. A problem is usually the starting point for a study. The researcher might then use these hypotheses to restate and clarify the study problem.

The null hypothesis and alternative hypothesis for the study problem should be expressed as a connection between two or more variables. The statements must express the relationship between two or more quantifiable variables, according to the requirements. For testing and describing relationships, the null hypothesis and alternative hypothesis should have obvious implications.

In statistics, you use a model to explain how the observed data may be generated. Typically the data is understood as realizations of a random variable with some distribution (binomial, Poisson, beta, Weibull, gamma, Gaussian etc.) and one is interested in the expected value (mean) of this distribution, that may depend on other factors (treatment, time, disease group, country, ...).

The expected value is formulated as a parametric function, being flexible enough to explain essentially any possible observation. The set of all posisble values of all the parameters in this model is called the parameter space. Now the observations you actually made may be more or less well explained by some combination of parameter values. A specific subset of the parameter space is called a (statistical) hypothesis. The complement of the parameter space (all values of all parameters not contained in this specific subset) is called the alternative. You determine if your observed data is considerably more likely under the alternative than under the hypothesis. A hypothesis test calculates the probability of a larger discrepancy of the likelihood of data under the hypothesis and the alternative under the assumption that your model is correct and that the true set of parameter values is in the subset of the parameter space you selected as the hypothesis. If this is very unlikely, then your observed data provides a considerable amount of information against yout model/hypothesis combination. If you believe that your model is actually ok, then the conclusion is that your hypothesis was a "bad choice" and the alternative, therefore, is the better choice. You would thus reject the hypothesis (in favour of the alternative, as this is "everithing elese that is not part of the hypothesis you just rejected).

We often test a point null hypothesis that divides the alternative into two parts to see if the data provide enough information to decide in which of these two parts the parameter value should be located.

Example:

You want to model the probability of a successful job application under a more or less rigorously defined set of circumstances (e.g. what job, who applies, and how the application is done and evaluated). You define the Bernoulli variable Y having the value 1 for a success and 0 for a failure of an application. The parameter is the success probability π, what here is identical to the expected value E(Y).

You collect data to learn something about π. Let's say you have single observation, and that person got the job, so the observed realization of Y1 is 1. This observation is most likeliy under the hypothesis π = 1 and it is least likely under the hypothesis that π = 0. Now you get a second observation, and that happened to be Y2 = 0. If these two observations were independent, then the combination of such two observations is most likely under the hypothesis that π = 0.5 and least likely unter the hypothesis that π = 0 as well as under π = 1. After many more (independent) observations you will have a set of data that is considerably more likely under a very narrow set of possible values for π than for any other posible value of π. In fact, it will be most likely for π = sum(Yi)/n, which is the average of the realizations, which I will denote with m (like "mean").

Testing a point hypothesis (π = h, with h being some value between 0 and 1) is now essentially comparing the maximum likelihood of the data under this hypothesis to the global maximum likelihood. As out simple model here has only a single parameter, the maximum likelihood under this hypothesis is just the likelihood of the data assuming π = h. The global maximum is obtained assuming π = m. The further away h is from m, and the narrower the likelihood peaks around m, the larger is the ratio of these two likelihoods, and the lower is the probability to obsere even larger likelihood ratios when π = h. If this is so unlikely that you are to reject π = h, then you reject any value of π that is even further away from m than h. This is because the likelihood has a single maximum (at π = m). So the test of π = h is equivalent to the test of π > h when h > m and equivalent to π < h when h < m. Hence, when rejecting π = h, you claim to have enough information from your observations about π to say whether π > h or π < h. If you cant reject π = h, then your data don't provide enough information wheter π > h or π < h.

So what we actually want is to see if we can distinguish π > h from π < h. We do this by testing the point hypothesis π = h. If we can reject this, then π = h is "out" to explain the observed data, because there is some other hypothesis within the alternative (which is any π ≠ h) under which the data is considerably more likely. And since the likelihood is maximal at π = m, we know that this value must be "on the same side" of h as m.

For more complex hypotheses, e.g. compound hypotheses about several parameters, there is no simple direction anymore, so the practical meaning of testing point hypothesis (where the point is a point in the multidimensional parameter space) may not be very obvious. But one can certainly define explicit subsets of the parameter space to test more "meaningful" hypotheses against more "meaningful" alternatives.

Hypothesis are assumptions and we try to verify the assumptions by using sample information. Null Hypothesis means a null assumption or simply you assume there is no such difference or relationship exist what you are looking for and alternative hypothesis assumes the difference or relation exist. In your research you only state alternative hypothesis which also called research hypothesis but you have to be idea about your null hypothesis also in your mind for the conclusion after the testing of hypothesis. Mostly we test the hypothesis for the possible acceptance of our alternative statement.