The researcher and the consultant: from testing to probability statements

Area of Science:

• Statistics
• Scientific Research Methods

Background:

• Traditional statistical inference methods include Fisher significance testing (ST) and Neyman-Pearson null hypothesis testing (NHT).
• Null hypothesis significance testing (NHST) emerged as a combination of ST and NHT.
• Bayesian statistics offers an alternative and complementary approach to NHST.

Purpose of the Study:

• To introduce practicing researchers to Bayesian inference.
• To build upon previous discussions on statistical inference models.
• To highlight Bayesian statistics as a viable alternative or complement to NHST.

Main Methods:

• Review of statistical inference principles.
• Explanation of Bayesian statistical concepts.
• Utilizing examples and discussions from prior work in the series.

Main Results:

• Bayesian inference is presented as a revived and increasingly advocated statistical approach.
• The article aims to clarify the practical application of Bayesian methods for researchers.
• The revival of Bayesian statistics is noted, with growing support in the research community.

Conclusions:

• Bayesian inference provides a valuable alternative or supplement to traditional null hypothesis significance testing.
• This article serves as a practical guide for researchers new to Bayesian statistical methods.
• Continued exploration of statistical inference models is crucial for robust scientific research.