Hypothesis Testing Definition
In statistics, hypothesis testing is an action in which securities or financial analysts test an assumption or a prediction related to a population parameter. There is no set method for hypothesis testing, as the nature of the data used and the reason for such testing determines what methodology will be employed by the analyst. Hypothesis testing is basically utilized in concluding a hypothesis that is conducted on a given set of data derived from a larger population.
A Little More on What is Hypothesis Testing
Analysts are faced with different hypotheses each and every day. While some of these hypotheses have some base to them, others don’t, and this is why analysts prefer to test all hypotheses related to their field. In hypothesis testing, analysts review a statistical sample with the aim of proving or disproving the existence of a null hypothesis. A null hypothesis generally refers to a hypothesis that primarily holds no ground whatsoever but sounds quite rational. Simply put, hypothesis testing helps analysts to know whether an unproved statement is true or not. In a case where it is true, the analysts would employ the methods within such a hypothesis in his research on a larger population, otherwise, he would find alternatives till he gets something that holds base.
- Hypothesis testing is used to draw conclusions to the result of a hypothesis that is conducted on a sample data from a larger population
- This test informs the analyst on the validity of his hypothesis
- In the case of statistics, analysts test a hypothesis by calculating and analyzing a random group of samples of the population that is been researched.
Statistical Hypothesis: Testing Process
In statistics, hypothesis testing is conducted by an analysts by measuring random sample of the population that is being examined. To conduct such a test, all analysts are expected to make use of random populations to test two different types of hypotheses: firstly the null hypotheses and then the alternative alternative hypotheses.
The null hypothesis sis the hypothesis which an analysts believes is valid. The alternative hypotheses is the one which the analyst believes to be untrue. However, there must be tests to show which one holds before they can reach any conclusion. There are also mutually exclusive events, thus if one hypothesis exists, the other won’t, and vice-versa.
Examples of Hypothesis Testing
Let us assume that an analyst wants to test the probability of a getting a either a six, a five, or a four on a coin. In this case, his null hypothesis might be yes (that it is possible to get any of these numbers in the first roll), while the alternative hypothesis would be no (it is not possible to get any of these numbers in the first roll). In a case like this, the null hypothesis would hold if the first roll produces either a six, a five, or a four on the first throw. However, if a two, a three, or a one appears, then the alternative hypothesis would be said to be true. In mathematical representations, the null hypothesis would be given as Ho: P = 3/6 or 0.5. The alternative hypothesis would be depicted as Ha: P x= 3/6 or 0.5 since they both have equal chances and they are mutually exclusive. Here x= means not equal to 50%.
Let us assume that 20 gamblers throw a dice using the details above. For clarity, we shall call a six, a five, and a four “upper dice” and a three, a two, and a one “lower dice.” Now, back to our example. Let us assume that 20 gamblers throw a dice 2 times each, and after the results have been counted, it is found that “upper dice” appeared 30% of the time, and “lower dice” appeared 70% of the time. In this case, the null hypothesis stated by the analyst above would be false and subsequently discarded because it is not equal to 50%. Here, the alternative hypothesis would be accepted. After this, the analyst would have to create another two sets of hypothesis, in which case the null hypothesis here would be the probability of getting up to 30% of “upper dice,” and the alternative hypothesis would be not getting up to 30% of “upper dice.”
Hypothesis Testing: The Four Steps Involved
There are four steps involved in hypothesis testing. The first step involves stating the null and alternative hypothesis. The second involves creating a plan for the evaluation of his hypotheses. The third step is the physical commencement of the analyzation of the sample data. And the final step is approving and disapproving the null hypothesis.