Statistics is a unique branch of Mathematics which deals with the collection, calculation and interpretation of numerical data. In Statistics, tests of significance are the method used to reach a conclusion to reject or support the claims based on sample data. The statistics are well known for research based on statistical surveys. During a statistical process, we come across certain terms such as "significance", "p-value" and others. Statistical tests are used to check whether the result of a study has the desired level of significance. This is done by using a statistical test that is appropriate for your situation. For example, if you’re testing whether an abstract treatment alone works better than a different treatment (control) then you would use an independent-groups design with an independent variable and dependent variable. The key point is that it should be random assignment so neither condition can have an impact on one of the other. This could, for example, be by chance or because it was not known at the outset in advance how many people would respond well or badly. If you wanted to compare two treatments but didn't know who got each treatment beforehand so it was arbitrary who got which treatment this wouldn't fit into this model as there was no way to assign them randomly (only chance). If you had large numbers of subjects then small differences in results could mean something statistically significant had been achieved even if no patient would notice any difference between treatments as there were enough subjects doing both and only tiny differences between patients are noticed when large numbers are involved from tiny changes
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