# Statistics/Testing Statistical Hypothesis

There are many different tests for the many different kinds of data. A way to get started is to understand what kind of data you have. Are the variables quantitative or qualitative? Certain tests are for certain types of data depending on the size, distribution or scale. Also, it is important to understand how samples of data can differ. The 3 primary characteristics of quantitative data are: central tendency, spread, and shape.

In the end, most folks summarize the result of a hypothesis test into one particular value - the p-value. If the p-value is smaller than the level of significance (usually $\alpha =5\%$ , but even lower in other fields of science i.e. Medicine) then you reject the null-hypothesis, but this does not mean you accept the alternative hypothesis. The p-value is essentially the probability of obtaining a test statistic at least as extreme as the one observed. If the p-value is greater than the level of significance you fail to reject the null-hypothesis, but this does not mean that the null-hypothesis is correct.