Statistical significance means only that you have ample data to conclude that an impact exists. It is a mathematical concept that does not know anything about the subject area and what constitutes a significant impact. Statistical significance is not comparable to scientific or therapeutic value, validity, meaningfulness, or any such synonyms (Bangdiwala 2016). Effects are said to be statistically important when the discrepancy between the hypothesized population parameter and observed sample statistic is substantial enough to indicate that it is unlikely to have occurred by chance. When making a report, the researcher has to start with a hypothesis, that is they must have some concept of what they think the result will be. An example would be a new drug to reduce blood pressure. The researcher hypothesizes that the new drug reduces systolic blood pressure by at least 10 mmHg relative to not taking the new medication. The hypothesis can be then mentioned, Taking the new medication would reduce systolic blood pressure by at least 10 mmHg as opposed to not taking the medication. In science, researchers can never prove any assertion since there are infinite alternatives as to why the result could have occurred. They can only attempt to disprove a particular theory. The researcher must then develop a question they can disprove before arriving at their conclusion that the new medication lowers systolic blood pressure. The researcher now has the null hypothesis for the study and must next determine the significance level or level of appropriate uncertainty (Abdelgawad 2020).
Reference
Abdelgawad, T. S. (2020). Statistical Significance. StatPearls Publishing LLC, 7(4):112-116
Bangdiwala, S. (2016). Understanding Significance and P-Values. Nepal journal Epidemiology, 6(1): 522524.
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