Null Hypothesis
Candlefocus EditorIn statistics, a null hypothesis is a statement that suggests that there is no difference between certain characteristics of a population or data-generating process. The hypothesis can be thought of as a starting point in which there is no evidence of any effect. Analysts test the hypothesis with data in order to provide evidence that may reject the hypothesis and offer support for an alternative hypothesis.
The hypothesis testing procedure provides researchers with the ability to reject or disprove the null hypothesis within a specified level of confidence. In order for the hypothesis to be rejected, data evidence must outweigh the presumption of the null.
Null hypothesis testing is an essential concept within the scientific method. This is because it is the basis of the principle of falsification. This principle states that a statement can be considered to be true only if it can be demonstrated that there is no evidence that it is false.
A common example of a null hypothesis is the ‘no effect’ hypothesis. This type of hypothesis is the basis for A/B testing, where the difference in performance between two treatments is examined. The hypothesis is that the two treatments will produce no difference in the observed performance, or put another way, that any observed difference was due to chance.
Researchers need to clearly state their null hypothesis and the criteria they will use to determine whether they can reject it. In addition, they must also consider any biases that may arise when collecting and analysing data.
Statistical hypothesis testing is an effective way of evaluating the validity of a hypothesis. A successful outcome, in which the data provide evidence of a statistically significant impact, will lead to the rejection of the null hypothesis. Analysts then must develop a new hypothesis that is more realistic and matches their observations. These hypotheses are then tested using the same method until a valid conclusion can be achieved.
In conclusion, a null hypothesis is a statement about the characteristics of a population or data-generating process that suggests no difference between them. Analysts test the hypothesis using the hypothesis testing procedure and can reject the hypothesis if the data provides sufficient evidence. Rejecting a null hypothesis leads to the formation of a new hypothesis and the process starts again.