Evaluating_P_Values (2)

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Evaluating P Values The P value is a significant value in the research process as it helps the researcher analyze the statistical significance of a hypothesis. This value is calculated using p-value tables. This value is used to determine whether the researcher will reject the null hypothesis. This value establishes evidence that helps the researcher promote the credibility of their studies. A p-value of 0.05 or lower is considered to be statistically significant. The p-value helps the researcher obtain evidence that will support their decision to reject the null hypothesis. This researcher uses the p-value to analyze the relationship between the various variables found in this data set. This p-value is represented as a proportion that is subject to the statistical test that the researcher uses to test their hypothesis. The p-value is representative of the different assumptions that are generated by the different tests. It's subject to the number of independent variables that define the data collected. It determines the statistical needs for the identified research. The p-value’s statistical significance is evident through the patterns that the research measures by analyzing the difference between the defined variables. The p-value recognizes the statistical significance of the data collected as the researcher tries to analyze the data collected from this data set. The p-value is also used to determine what studies get published or funded. The researcher identifies the process of testing statistical significance that is determined by the difference between the various variables. There is a possibility that there is no difference between these variables hence there is no effect. This lack of difference is defined as the null hypothesis and it is used to measure how compatible the researcher’s data is with the null hypothesis. The P value plays a significant role in evidence-based medicine as it guides the decision- making process. It tests the research hypothesis with the results provided as it tries to determine the statistical significance. Researchers who have issues with evaluating the p-value are encouraged to consider using the confidence intervals to determine the statistical significance of this data set. This researcher recognizes the need to use the p-value in the scientific decision- making process. This decision-making process is subject to the p-value determining the

statistical significance of this data. The p-value falls short as does not provide strong evidence. Therefore, most researchers suggest that this value should be replaced with confidence intervals. The p-value is used by the researcher to quantify the evidence against a null hypothesis. It plays a significant role in hypothesis testing as it determines the significance of the result in relation to the null hypothesis. The p-value is subject to the probability value and is expressed between 0 and 1. The p-value informs us how likely this researcher would see the data they observed if the null hypothesis was true. A p-value less than or equal to the researcher's significance level indicates strong evidence against the null hypothesis. It allows any researcher in the social sciences to analyze the systematic procedure used to observe, describe, predict, and explain a specific behavior. The p-value is significant in experiments, case studies, surveys, and naturalistic observations. Therefore, this research ensures that the data collected is objective and reliable. Therefore, the p-value plays the of verifying or disapproving this hypothesis. Reference Andrade, C. (2019). The P value and statistical significance: misunderstandings, explanations, challenges, and alternatives. Indian journal of psychological medicine, 41(3), 210-215.

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