# Significance of hypothesis in research

Jump to navigation Significance of hypothesis in research to search For the publication, see Null Hypothesis: The Journal of Unlikely Science.

In inferential statistics, the null hypothesis is a general statement or default position that there is no relationship between two measured phenomena, or no association among groups. The null hypothesis is generally assumed to be true until evidence indicates otherwise. 0, never the upper-case letter of the alphabet O. The concept of a null hypothesis is used differently in two approaches significance of hypothesis in research statistical inference. In the significance testing approach of Ronald Fisher, a null hypothesis is rejected if the observed data are significantly unlikely to have occurred if the null hypothesis were true.

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In the hypothesis testing approach of Jerzy Neyman and Egon Pearson, a null hypothesis is contrasted with an alternative hypothesis and the two hypotheses are distinguished on the basis of data, with certain error rates. Statistical inference can be done without a null hypothesis, by specifying a statistical model corresponding to each candidate hypothesis and using model selection techniques to choose the most appropriate model. Hypothesis testing requires constructing a statistical model of what the data would look like, given that chance or random processes alone were responsible for the results. The hypothesis that chance alone is responsible for the results is called the null hypothesis.

The model of the result of the random process is called the distribution under the null hypothesis. Hypothesis testing works by collecting data and measuring how likely the particular set of data is, assuming the null hypothesis is true, when the study is on a randomly selected representative sample. If the data do not contradict the null hypothesis, then only a weak conclusion can be made: namely, that the observed data set provides no strong evidence against the null hypothesis. For instance, a certain drug may reduce the chance of having a heart attack.

Possible null hypotheses are «this drug does not reduce the chances of having a heart attack» or «this drug has no effect on the chances of having a heart attack». The test of the hypothesis consists of administering the drug to half of the people in a study group as a controlled experiment. The null hypothesis and the alternative hypothesis are types of conjectures used in statistical tests, which are formal methods of reaching conclusions or making decisions on the basis of data. The statement being tested in a test of statistical significance is called the null hypothesis.

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