Scholars emphasize the significance of the p-value in scientific research and statistics. P-value is the probability of receiving the result that is at least as extreme as the one mentioned in the sample data with the assumption that the null hypothesis is true. When the p-value is less than the significance level, or when it matches the significance level, the null hypothesis should be rejected. Typically, the p-value ≤ of 0.05 means that there is strong evidence against the null hypothesis.
In the given scenario, the results indicated that Χ2(4) = 2.35, p = 0.012. Such findings make it possible to reject the null hypothesis. Understanding the null hypothesis is significant since it allows the researcher to comprehend the p-value. The null hypothesis is the lack of disparity between the groups that are being tested by the researcher. The null hypothesis plays a crucial role when the results of an experiment are assessed.
The p-values indicate to what extent the data are consistent with the null hypothesis. A high p-value shows that the data is likely if the null hypothesis is true. A low p-value indicates that the sample gives sufficient evidence to reject the null hypothesis for the whole population. As it is evident from the scenario, the p-value is low. The interpretation of the p-value is performed when a researcher asks a question, “what is the likelihood of data presuming that the null hypothesis is true?”. It is important to note that the p-value does not evaluate the strength of the alternative hypothesis. As a result, it is obvious that the low p-level in the given scenario proves that the null hypothesis can be rejected.