Using pValue to Measure Significance Level of Continuous Data

In this paper, we provide a practical method on how to calculate pValue for continuous data. In later paper, we will cover pValue for discrete data. For continuous distribution in part 1, we defined pValue as 1 - CDF; for discrete distribution in part 2, we define pValue as 1 - MDF. The purpose of this paper is to provide a practical guide on how to calculate the pValue if the sample size, and critical value for T or Z are known. The methodology has practical value to researchers in both natural and social science. All quantitative research requires the reporting of statistical significance. The T and Z values are known in the field. We claim new discovery for the method on how to calculate pValue through the TZ conversion equation. We proposed three steps in calculating pValue: (i) converting chi square or F to T, (ii) with known T, obtain Z by using the TZ conversion equation Z = (1.15T – 1.64) + 1.40, and (iii) with known Z, determine F(Z). The pValue is simply 1 – F(Z). This procedure is a contribution to the field because it serves as an effective tool for editorial and peer reviews in research publication. We present three tables for converting test statistics T, chi square, and F to pValue.