**Hypothesis** is making an assumption. In **Statistics**, a **Hypothesis** or an assumption is taken first and then the Hypothesis is tested whether it is accurate or not. Hypothesis testing is a study based on statistical accuracy of an experiment. If the result is positive, then it is called statistically significant.

There are two types of statistical hypotheses. A Null Hypothesis and an Alternate Hypothesis. A Null Hypothesis is denoted by H0, it is actually an assumption that the simple observations are purely from chance.

Alternate Hypothesis on the other hand is denoted by H1 or Ha, assumes that that sample is influenced by a non-random cause.

An example for Hypothesis Testing.

Suppose that we want to test the hypothesis with a significance level of .05 that the climate has changed since industrialization. Suppose that the mean temperature throughout history is 50 degrees. During the last 40 years, the mean temperature has been 51 degrees and suppose the population standard deviation is 2 degrees. What can we conclude?

We have

H0: µ = 50 or the temperature is normal

H1: µ ≠50 or the temperature has changed

We compute the z score:

(51-50)/(2/√40) = 3.16

The table gives us 0.9992

So that p = (1 – 0.9992)(2) = 0.002

Since 0.002

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This article is in continuation with our previous articles on **Statistics Regression** and **Statistics Correlation**