Statistics and Statisticians for Social Sciences

Personalities: portraits and biographies Introduction to Statistics: key concepts Hypothesis testing and statistical errors Statistical estimations: important measures Statistics made easy:
video illustrations
Links and acknowledgements

Humankind created many useful and nicely written materials on hypotheses testing; so, there is no need for reinventing bicycle.

Here are some useful links
for getting know literally everything in the arts of hypothesis testing :)
Statistical errors are discussed in all the above-listed texts - we all know that there is no testing without errors.
I will just introduce the Two Types of Statistical Errors in two simple pictures:

1. hypothesis testing and statistical errors

A Type I error is often represented by the Greek letter alpha (α) and a Type II error by the Greek letter beta (β). In choosing a level of probability for a test, you are actually deciding how much you want to risk committing a Type I error—rejecting the null hypothesis when it is, in fact, true. For this reason, the area in the region of rejection is sometimes called the alpha level because it represents the likelihood of committing a Type I error.

While  we can do nothing with one of the errors, we can easily avoid the other by consciouschoice of a statistical criteria for testing a hypothesis.
For making the right choice, youl just need to be aware of measurement issues (types of scales and levels of measurement).
Having them in mind, please follow these simple guidelines to choose a critetia for testing your hypothesis:

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