A null hypothesis is a statement that assumes no significant relationship, correlation, or difference between multiple variables within a population. It’s the default assumption unless empirical evidence proves otherwise. In statistics, the null hypothesis is usually represented by the symbol "H0". Usually, the researcher tries to provide evidence to reject the null hypothesis. For example, if you were testing whether there is a difference between two foods' effects on humans' health, the null hypothesis would be, "There is no difference between the two foods and their impact on humans' health." You would then collect sample data and run statistical tests on that data to see if you can reject the null hypothesis or not.
The null hypothesis is useful because it can tell us whether the results of our study are due to random chance or the manipulation of a variable (with a certain level of confidence). A null hypothesis is rejected if the measured data is significantly unlikely to have occurred and a null hypothesis is accepted if the observed outcome is consistent with the position held by the null hypothesis. Rejecting the null hypothesis sets the stage for further experimentation to see if a relationship between two variables exists. Hypothesis testing is a critical part of the scientific method as it helps decide whether the results of a research study support a particular theory about a given population. Hypothesis testing is a systematic way of backing up researchers’ predictions with statistical analysis.
Null hypotheses (H0) start as research questions that the investigator rephrases as statements indicating no effect or relationship between the independent and dependent variables. It is a default position that your research aims to challenge or confirm.