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Hypothesis Testing

Hypothesis is essentially an assumption made about a certain parameter pertaining to the underlying population which is being studied. This assumption may or may not be true. Hypothesis testing refers to the statistical procedure of finding out if this is true or not. In other words, hypothesis testing enables us to accept or reject the statistical hypothesis.

The choice of methodology to be deployed to undertake testing of the hypothesis largely depends on the nature of the data used and the reason for the analysis. The sample under consideration on which the testing is done, is derived from the population as in many cases it is not feasible to study the entire population. If sample data are not consistent with the statistical hypothesis, the hypothesis is rejected. The analyst continues to run the test on several hypotheses till he finally arrives at the hypothesis which is true.

There Are Two Types Of Statistical Hypotheses

Null hypothesis- The null hypothesis is represented as H0, infers that the sample observations result purely from chance.

Alternative hypothesis- The alternative hypothesis, denoted by H1 or Ha, is the hypothesis that sample observations are influenced by some specific cause.

Let us take the example of flipping a coin. Here the hypothesis is to understand if the coin is fair and balanced. Given this, the null hypothesis would typically be that 50 percent of the times, it would be Heads while the balance 50 percent would be Tails.

For example, suppose we wanted to determine whether a coin was fair and balanced. Statistically these hypotheses would be denoted as

H0: P = 0.5 

Ha: P ≠ 0.5

Now when the coin is actually flipped 50 times and the outcome recorded was 40 heads and 10 tails, it would be concluded that the coin was probably not fair and balanced, in other words we would tend to reject the null hypothesis.

Hypothesis testing is a large topic requiring in-depth understanding of various concepts such as null and alternate hypotheses, standard errors, Type 1 and 2 errors, etc. Other concepts encompass Hypothesis Testing for a single population mean etc.

Hypothesis testing usually begins with defining the two hypotheses so that only one can be right. Thereafter an analyst needs to decide how data will be looked into and in some cases physically analyse the sample data. Once sample data is in place, the analyst would need to conduct the test and arrive at the conclusion.

All in all it's a complex process of understanding the theory of the subject and then applying it to solve practical problems. Keeping this in mind, we have carefully designed the course for students at all levels. They need not have a background in statistics. We also understand that some students are not quantitatively inclined but require undertaking these courses and having a god understanding of the subject on the way to achieve their professional goals.

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Our tutors who are at least Post graduates or PhDs in the field of mathematics and statistics will be able to teach you concepts right from basics to higher level of the subject matter as per the demands of your curriculum. It is a continuous effort on our part to upgrade and broaden our tutoring curriculum to accommodate the changes happening in the industries so that our learners are able to apply the concept very well in real life working scenarios.

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