Hypothesis testing

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A hypothesis (plural hypotheses) can be defined as a proposed explanation that is made following some evidence and is often considered as a starting point of further investigation. Synonyms which have the same meaning that can define hypothesis include; thesis, proposition, presumption, idea and theorem among-st others. However, it is important to note that although there is a close relationship of hypothesis vs theory, there is a difference between the two. A hypothesis is said to be scientific if it can be tested using scientific methods while a scientific theory is coherent explanation for a large number of facts and observations about the natural world. The action of stating a it is referred to as hypothesizing (to hypothesize).

What is hypothesis testing?

There are two branches of statistics: Descriptive statistics and inferential statistics. Descriptive statistics involves description of data using typical measurements such as mean, frequency and range while inferential statistics involves drawing a conclusion about a given population using a representative sample. The process of testing is key element of inferential statistics can be described as the process of rejecting or accepting the null hypothesis using relevant data and relevant statistical techniques. Below are the steps involved in testing;

Type 1 Versus Type 11 errors

It is not complete to talk about hypotheses and forget to discuss the type I and II errors. These are the errors that occur when statistical significance tests are used to make binary decisions about the null hypothesis. Type I errors occurs when the researcher rejects the null hypothesis when indeed it is true. On the other hand, the type II error occurs when a researcher fails to reject the null hypothesis when it is actually false. In majority of Statistics books and references, type I error is usually abbreviated as α while the probability of committing a type II error is denoted by β. The risk of committing type I error is usually based on the alpha level of significance. If the level of significance is 0.05, then there is a 5% risk of committing type I error. Theoretically, the risk of committing type I error can be reduced by using a smaller level of significance such as the 0.01 instead of 0.05.The risk of type II error depends on factors such as the sample size. However, it is important to note that factors that increase the statistical power significantly reduce the risk of type II error.

For characteristics of hypothesis testing check below

Hypothesis testing has particular characteristics that should be followed. Here is a link that discusses these characteristics of hypothesis testing in detail

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