Null Hypothesis Significance Test, P-Value, A Priori Procedure And Gain-Probability Diagram

Abstract

The null hypothesis significance test (NHST) has traditionally served as the default statistical technique used for deciding between two contradictory hypotheses, especially concerning population means. However, this technique, along with its associated p-value and confidence intervals, come with many shortcomings and limitations. This talk explores alternative techniques, namely the a priori procedure (APP) and the gain-probability (G-P) analysis.

Before data collection, we will consider two important questions: (1) "How closely does the researchers desire the sample mean to approximate the population mean?" and (2) "With what probability does the researcher aim for the sample mean to fall within the specific distance of the population mean?". By specifying the precision for (1) and the confidence for (2), we can determine the necessary sample size across various distributional settings. Numerically examples will be provided for illustration.

Presenter

Lisa Hillas joined the University of Auckland as a Lecturer in 2023, after completing her PhD in Operations Management at The University of Chicago, Booth School of Business. Before starting her PhD, she completed her undergraduate studies in Engineering Science and Philosophy at the University of Auckland.

Lisa is interested in researching questions regarding the design of systems with strategic agents. Her current research looks at the design of queueing or waitlist systems, with applications to public housing and refugee housing.