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Hypothesis testing Hypothesis testing is a testing of decision predicted or a statement in order to accept or reject of any statement to make an authentic decision. In any of the hypothesis testing two error should be taken into under consideration. Type one error Type two error In all types of hypothesis testing type one error is more serious than type two error. Hence, decision maker in any scientific investigation tries to control type one error with more attention. The controlling of type one error is related with the significance level of the test at which the tests are ongoing. Let us set up hypothesis as Null hypothesis = 50 Alternative hypothesis not equal to 50 The sample mean is x The condition of rejecting of Null hypothesis is only when sample mean is greater than 53 or less than 47. But, if the sample mean place between 47 to 53 that is closer to 50, is not strong evidence of rejecting null hypothesis. A null hypothesis can be reject in significance

Before explaining about Null Hypothesis, let us discuss about the conception of Hypothesis. Hypothesis Hypothesis is a statement about a population parameter or population distribution. Any statement that should be tested for discovering the authenticity in order to making a decision is Hypothesis. Any claim by a producer against his product is simply a hypothesis that should be tested by the customer that the claim is true or not in order to make a decision to purchase or not. Hypothesis testing Two set of hypothesis may found in a hypothesis testing. Null hypothesis and alternative hypothesis. The true statement from a pair of hypothesis is termed as Null hypothesis and other statement is alternative hypothesis. Hypothesis testing is used for making a decision to accept or reject any of the statement. Accepting or rejecting of statement is depends of the true evidence against the statement. Let consider  about any statement of any product stability claimed by produc