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Generalisation and Hypothesis In Mass Communication Research! What Is The Difference?

First of all let us look at the two concepts;
Generalisation is relating what you found in smaller groups or samples to a lager population. It is when you make broad inferences from findings on a smaller sample or specific observations. Most times, you carry out a study on a small group so you can generalise the result to a larger group. That is why the selection of sample size should be representative of the entire population so that you can generalise the results to a larger group. Generalisation is backed up by empirical evidence. For instance, let us assume you are doing a study on the influence of social media on Chukwuemeka Odumegwu Ojukwu University (COOU) students’ academic performance. If your sample size is 400 for instance, and you ensured that the selected sample is representative of the entire population, when you finish the study you can generalise the findings, that is, relate it to the entire students of COOU. If for instance, you found that exposure to social media negatively affects students’ academic performance, based on the 400 students you studied, you can generalise the result to the entire students of the university. It then means students of chukwuemeka odumegwu ojukwu university do not do well in class because of excessive use of social media. That is what generalisation means in primary terms.
Hypothesis on the other hand, refers to a statement of proposition that does not have empirical evidence yet. (When you here empirical evidence it means information, data or facts you got from observation, that is, information from research you carried out on a particular issue).
Hypothesis is a tentative statement which you intend to confirm or test during your study. You are saying that something is possibly like this but you have to confirm first before you can make it a statement of fact. It is a supposition you intend to confirm by testing it in a study. It could be true or false, that is why it is called a tentative statement. You are simply predicting that something is like this or like that but you cannot rely on it until it is tested. So it is just a proposed explanation of a situation or a phenomenon. You basically have the Null and Alternative hypothesis. One is saying that yes this is like this, the other one is saying no it is not like that. When you now carryout the research and test the hypothesis you will now know whether the statement is true or false, that is, whether to accept the alternative hypothesis (which means the statement is true) or the null hypothesis (which means the statement you proposed is false). Hypothesis is often used to test the relationship between to variables.
Example: Let us use our initial example on students of COOU and social media to give example of hypothesis. We are studying the influence of social media on academic performance of COOU students. The Null hypothesis will say “There is no significant relationship between exposure to social media and academic performance of COOU students” while the alternative hypothesis will say “There is a significant relationship between exposure to social media and academic performance of COOU students.” The Null hypothesis is represented with the symbol “Ho” while the alternative hypothesis is represented with the symbol “H1”.
The basic difference between the two terms – generalisation and hypothesis – is that you generalise what you have facts to back up but hypothesis is still at tentative level or unconfirmed state, so you can’t generalise it. What you are generalising has already been confirmed in a study on smaller units or sample. There is empirical evidence to support what you want to generalise. But the hypothesis is not yet a fact so you can’t relate it to a larger population until you have tested it. You only generalise hypothesis after you have tested it.

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