References
- Arrow, K. J. (1960), “Decision Theory and the Choice of a Level of Significance for the t-Test,” in Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling (Vol. 2), ed. I. Olkin, Stanford, CA: Stanford University Press, pp. 70–78.
- Cohen, J. (2013), Statistical Power Analysis for the Behavioral Sciences (2nd ed., Ebook), New York: Routledge.
- Fisher, R. A. (1956), Statistical Methods and Scientific Inference, London: Hafner Publishing Co.
- Harford, T. (2014), “Big Data: A Big Mistake?,” Significance, 11, 14–19. DOI: 10.1111/j.1740-9713.2014.00778.x.
- Harvey, C. R. (2017), “Presidential Address: The Scientific Outlook in Financial Economics,” The Journal of Finance, 72, 1399–1440. DOI: 10.1111/jofi.12530.
- Johnstone, D. J. (1986), “Tests of Significance in Theory and Practice,” Journal of the Royal Statistical Society, Series D, 35, 491–498. DOI: 10.2307/2987965.
- Keuzenkamp, H. A., and Magnus, J. R. (1995), “On Tests and Significance in Econometrics,” Journal of Econometrics, 67, 5–24. DOI: 10.1016/0304-4076(94)01624-9.
- Kim, J. H. (2019), “OptSig: Optimal Level of Significance for Regression and Other Statistical Tests,” R Package Version 2.0, available at https://CRAN.R-project.org/package=OptSig.
- Kim, J. H., Ahmed, K., and Ji, P. I. (2018), “Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence,” Abacus, 54, 524–546. DOI: 10.1111/abac.12141.
- Kim, J. H., and Choi, I. (2020), “Choosing the Level of Significance: A Decision-Theoretic Approach,” Abacus, in press. DOI: 10.1111/abac.12172.
- Kim, J. H., and Ji, P. I. (2015), “Significance Testing in Empirical Finance: A Critical Review and Assessment,” Journal of Empirical Finance, 34, 1–14. DOI: 10.1016/j.jempfin.2015.08.006.
- Leamer, E. (1978), Specification Searches: Ad Hoc Inference With Nonexperimental Data, A Wiley-Interscience Publication, Ann Arbor: Wiley, available at https://books.google.com.au/books?id=sYVYAAAAMAAJ
- Mudge, J. F., Baker, L. F., Edge, C. B., and Houlahan, J. E. (2012), “Setting an Optimal α That Minimizes Errors in Null Hypothesis Significance Tests,” PLoS One, 7, e32734. DOI: 10.1371/journal.pone.0032734.
- Ohlson, J. A. (2015), “Accounting Research and Common Sense,” Abacus, 51, 525–535. DOI: 10.1111/abac.12059.
- R Core Team (2019), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing, available at https://www.R-project.org/.
- Selvanathan, E. A., Selvanathan, S., and Keller, G. (2017), Business Statistics: Australia/New Zealand (7th ed.), South Melbourne, Victoria: Cengage Learning.
- Wasserstein, R. L., and Lazar, N. A. (2016), “The ASA Statement on p-Values: Context, Process, and Purpose,” The American Statistician, 70, 129–133. DOI: 10.1080/00031305.2016.1154108.
- Ziliak, S., and McCloskey, D. N. (2008), The Cult of Statistical Significance: How the Standard Error Costs us Jobs, Justice, and Lives, Ann Arbor: University of Michigan Press.