Question

Describe why hypothesis testing is important to businesses?

Answer 1

Answer :

Hypothesis testing is a procedure of refuting a presumption right or wrong.

The suspicions are either founded on specific realities or thought to be checked through testing.

The invalid theory and elective speculation are chosen dependent on consequence of the theory , A business research makes a great deal of presumption and base their decision on choice of any of these hypothesis  to demonstrate their outcomes .

Hypothesis testing can be used in business applications to help validate an assumption being made about data relationships. This lesson looks at the process of hypothesis testing and provides an example of its use.

The legal concept that one is innocent until proven guilty has an analogous use in the world of statistics. In devising a test, statisticians do not attempt to prove that a particular statement or hypothesis is true. Instead, they assume that the hypothesis is incorrect (like not guilty), and then work to find statistical evidence that would allow them to overturn that assumption.

In statistics this process is referred to as hypothesis testing, and it is often used to test the relationship between two variables. A hypothesis makes a prediction about some relationship of interest. Then, based on actual data and a

pre-selected level of statistical significance, that hypothesis is either accepted or rejected.

Hypothesis Testing Process

Hypothesis testing follows a general series of steps:

• Create a null hypothesis and an alternative hypothesis.
• Define what statistic will be used to test the hypothesis.
• Specify a level of statistical significance that will be used. 5% and 1% are commonly used values.
• Gather or collect data and perform the associated statistical calculations.
• Apply the statistical significance rule to reject or accept the null hypothesis.

The Null and Alternative Hypotheses

To begin, we construct what is known as the null hypothesis, H0, which is often constructed in to indicate that there is not a statistical difference between two populations or measurements. The alternative hypothesis, HA, states the opposite. These two statements are mutually exclusive, so that accepting or rejecting one has the opposite effect on the other.

Stated another way, the null hypothesis essentially states that the effects being measured could reasonably arise from chance alone. It's assumed to be true until proven otherwise, and the goal of hypothesis testing is to assess whether or not we should reject the null hypothesis.