Mark Rubin

Hypothesising after the results are known

Personalise
A group of three young women and two men of different ethnicities are in a business meeting in a modern day office. A bald man is talking to the group while there are laptops and documents on the table.
Time

29/05/2018 - 12:00 - 13:00

Address

Room 464, UNSW Business School, UNSW

Description

  • May 29, 2018
  • Speaker: Mark Rubin
  • Topic: Hypothesising After the Results are Known (HARKing): Are all Types of HARKing Bad for Science Under all Conditions?

Abstract

Hypothesizing after the results are known, or HARKing, occurs when researchers check their research results and then add or remove hypotheses on the basis of those results without acknowledging this process in their research report (Kerr, 1998). HARKing has been proposed as one of the causes of the replication crisis in science. In this presentation, I consider whether all types of HARKing are bad for science under all conditions.

In particular, I consider three forms of HARKing: (a) using current results to construct post hoc hypotheses that are then reported as if they were a priori hypotheses; (b) retrieving hypotheses from a post hoc literature search and reporting them as a priori hypotheses; and (c) failing to report a priori hypotheses that are unsupported by the current results. I consider the conditions under which each of these three types of HARKing is most and least likely to be bad for science. I conclude with a brief discussion about the ethics of each type of HARKing.

About the speaker

Mark Rubin is an associate professor in social psychology at the University of Newcastle, Australia. He received a Master’s degree from the London School of Economics and a PhD from Cardiff University, UK. He is best known for his work on social identity and intergroup relations, including research on prejudice and stereotyping. His recent work has considered the causes of the replication crisis in psychology and beyond, including hypothesising after the results are known, the use of significance testing in exploratory research situations, and the problem of sample-contingent data analyses.