Hypothesis testing is a statistical approach used in making decisions about data. In hypothesis testing, there are two hypotheses that are posed by the researcher and they are…

- Null hypothesis-There is no difference between the sample population and the statistical population in relation to the mean or some other parameter that is being assessed
- Alternative hypothesis-There is a difference between the sample population and the statistical population in relation to the mean or some other parameter that is being assessed

Generally, researchers often hope to reject the null hypothesis which indicates that the alternative hypothesis is correct. However, strictly speaking, a researcher never accepts any hypothesis. Instead, you reject or you do not reject the null hypothesis. This is because further testing will always be needed to confirm the results.

How to know whether to reject or not reject the null depends on the results of the analysis. A researcher needs to select a level of statistical significance which is usually 1%, 5%, or 10%. The significance level changes the size of the rejection region at the tails of the normal distribution. The lower the significance level the smaller the rejection region which influences the interpretation of the results. To reject a null hypothesis, the results of the analysis must fall within the rejection region.

After determining the level of significance a researcher analyzes the data to determine the results. The results then need to be interpreted by stating them in simple English. From this, the researcher can develop a conclusion about what the results mean.

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andydevil12Reblogged this on andydevil12 and commented:

under research!!!

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liamgavinmurrayNice, short and sweet. Would love a few examples aswell testing against a given distribution e.g. z-testing.

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