Accounting A statistical hypothesis is also known as confirmatory data analysis which is testable on the basis of observing a process that is modeled via a set of random variables. Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter.

There are two different hypothesis viz. Null hypothesis and alternative hypothesis.  The null hypothesis is the hypothesis that the analyst believes to be true. The alternative hypothesis is the hypothesis the anlyst believes to be untrue. In the other words, when Null hypothesis is true, alternative hypothesis making it effectively the opposite.  It means these two hypothesis are mutually exclusive, and only one can be true. However, one of the two hypotheses will always be true.

Let us take an example to understand Null hypothesis and alternative hypothesis.  If you toss a Rupee coin, there are two possibilities; the coin has chances of landing either heads or the tails. Here, the coin has 50% chances of landing Heads, if you say yes, the null hypothesis would be yes, and the alternative hypothesis would be No. Mathematically, the null hypothesis would be represented as “yes”: P = 0.5. The alternative hypothesis would be denoted as “No” and be identical to the null hypothesis, except with the equal sign struck-through, meaning that it does not equal 50%.

In Hypothesis testing, if the significance value of the test is greater than the predetermined significance level, that is more than 50% then we accept the null hypothesis. If the significance value is less than the predetermined value, then we should reject the null hypothesis. Let us take another example.  Suppose, the 10 coin flips were distributed as 4 heads and 6 tails, the analyst would assume that a coin does not have a 50% chance of landing heads, and would reject the null hypothesis and accept the alternative hypothesis. Afterward, a new hypothesis would be tested, this time that a coin has a 40% chance of landing heads. Thus, all hypotheses are tested using a four-step process. The first step is for the analyst to state the two hypotheses so that only one can be right. The next step is to formulate an analysis plan, which outlines how the data will be evaluated. The third step is to carry out the plan and physically analyze the sample data. The fourth and final step is to analyze the results and either accept or reject the null hypothesis.

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