UNDERSTANDING THE RUDIMENTS OF HYPOTHESIS TESTING

Hypothesis is simply defined as an ‘educated guess’. One of the areas that have been a challenging aspect of any project work is the hypothesis testing. Testing a hypothesis in most project work is simply a prelude to the data analysis or simply put it’s just a prerequisite to the actual data analysis of the project work. For most researchers or students to be able to score an A in their project work, then chapter four must have to contribute immensely to this and it is interesting to note that the chapter four which as to do with data analysis and interpretation houses the hypotheses

Basically, hypothesis is of two types which will be basically discussed here; they are:

The Null Hypothesis

The null hypothesis which is usually denoted by H0 is simply the assertion that something or an experiment the researcher is embarking on is ‘null’ is zero or no interaction. The null hypothesis states that a population parameter is equal to a value. The null hypothesis is placed first professionally in testing a hypothesis. The null hypothesis simply means the hypothesis that has no either relationship or interaction.

The Alternative Hypothesis

The alternative hypothesis simply denoted by H1. It is an assertion that the relationship or interaction in an experiment is not zero. The alternative hypothesis as the name indicates is the direct opposite of the null hypothesis. On another thought, the alternative hypothesis indicates that the experiment under study is different than the value of the experiment in the null hypothesis. The alternative hypothesis is what you might believe to be true or hope to be proven true.

Testing a hypothesis should generally be in this format for most research or project work

H0: there is NO interaction or relationship between A and B

H1: there is an interaction or relationship between A and   B

One can see that the null hypothesis is a direct opposite of the alternative hypothesis

In conclusion, a hypothesis test is a statistical test that is used to determine whether there is enough evidence in a sample of data to make an inference that a certain condition is true for the entire population under study in your project work.

A hypothesis test examines two opposing hypotheses about a population: the null hypothesis and the alternative hypothesis. The null hypothesis is the statement being tested. Usually the null hypothesis is a statement of "no effect" or "no difference". The alternative hypothesis is the statement you want to be able to conclude is true in your project work analysis.

The decision to either accepting or rejecting either the null or alternative hypothesis is done using the level of significance alongside the p-value. If the p-value is less than the level of significance, we reject the null hypothesis and accept the alternative. If the p-value is greater than the level of significance we accept the null hypothesis.