The null hypothesis can be rejected based on the results of a statistical test. In general, if the p-value (probability value) associated with a statistical test is less than a predetermined significance level (usually 0.05 or 0.01), the null hypothesis is rejected in favor of the alternative hypothesis.
The null hypothesis is typically formulated as a statement of no difference or no relationship between variables. When analyzing data, statistical tests are used to assess whether the observed data provides enough evidence to reject this null hypothesis and support an alternative hypothesis.
Once the data is collected and analyzed using an appropriate statistical test, the resulting p-value is compared to the chosen significance level. If the p-value is smaller than the significance level, it indicates that the observed data is unlikely to have occurred by chance alone, and the null hypothesis is rejected in favor of the alternative hypothesis.
It is important to note that rejecting the null hypothesis does not prove the alternative hypothesis to be true, but rather suggests that there is sufficient evidence to support it. The null hypothesis should only be rejected if the evidence against it is strong enough based on the specified significance level.
How null hypothesis can be rejected
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