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| Evaluating nileffect in psychological research: A practical primer |
| XU Yue-pei1,2,LU Chun-lei3, WANG Jun4,SONG Qiong-ya4,JIA Bin-bin5,HU Chuan-peng6 * |
| (1 CAS KeyLaboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101) (2 Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049) 3Institute of Psychological and Brain Sciences, Zhejiang Normal University, Jinhua321004； 4 Department of Psychology, Sun Yat-Sen University, Guangzhou510006； 5 School of Psychology, Shanghai University of Sports,Shanghai200438； 6 Schoolof Psychology, Nanjing Normal University, Nanjing 210097） |
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Abstract In psychological research, researchers may need to provide evidence for the non-existence of effects (i.e., nil effect). However, the most widely used statistical inference framework in psychology, the null hypothesis significance test (NHST), cannot provide evidence for nil effects. In other words, a non-significant p-value (e.g., p> .05) cannot be interpreted as evidence that supports the nil hypothesis. As a result, in practice, researchers either avoid interpreting the non-significant results, or mis-interpret the non-significant results as evidence for the nil hypothesis. Here we introduced three methods that can evaluate nil effects, the equivalence test, Bayesian estimation, and Bayesian factor (BF), to Chinese researchers. We illustrated how to apply these methods to real psychological data using two open datasets. This primer may help Chinese researchers to improve their statistical inference for non-significant results.
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Published: 13 October 2021
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