The researcher then looks to see how the blood pressure changes after receiving the new medication and performs a statistical analysis of the results to obtain a p-value (probability value). In the example, the researcher would give some individuals the new medication and other individuals no medication. Now the researcher can perform their research. We now have the level of uncertainty the researcher is willing to accept (alpha or significance level) of 0.05 or 5% chance they are not correct about the outcome of the study. For the current example, the alpha is 0.05. The alpha is the decimal expression of how much they are willing to be wrong. Thus, the researcher who wants to be 95% sure about the outcome of their study is willing to be wrong 5% of the time about the study result. Probabilities are stated as decimals, with 1.0 being completely positive (100%) and 0 being completely negative (0%). ![]() Our researcher wants to be correct about their outcome 95% of the time, or the researcher is willing to be incorrect 5% of the time. The significance level is given the Greek letter alpha and specified as the probability the researcher is willing to be incorrect. The researcher must then settle for some level of confidence or the significance level for which they do want to be correct. Thus, the null hypothesis for our researcher would be, "Taking the new medication will not lower systolic blood pressure by at least 10 mmHg compared to not taking the new medication." The researcher now has the null hypothesis for the research and must next specify the significance level or level of acceptable uncertainty.Įven when disproving a hypothesis, the researcher will not be 100% certain of the outcome. The hypothesis, to be disproven, is the null hypothesis and typically the inverse statement of the hypothesis. The researcher must then formulate a question they can disprove while coming to their conclusion that the new medication lowers systolic blood pressure. They can only try to disprove a specific hypothesis. The hypothesis can be then stated, "Taking the new medication will lower systolic blood pressure by at least 10 mmHg compared to not taking the medication." In science, researchers can never prove any statement as there are infinite alternatives as to why the outcome may have occurred. The researcher hypothesizes that the new medication lowers systolic blood pressure by at least 10 mmHg compared to not taking the new medication. ![]() ![]() We will use the example of a new medication to lower blood pressure. When creating a study, the researcher has to start with a hypothesis that is, they must have some idea of what they think the outcome may be. If we break apart a study design, we can better understand statistical significance. In research, statistical significance is a measure of the probability of the null hypothesis being true compared to the acceptable level of uncertainty regarding the true answer.
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