Someone asked me how to compute one-sided p-values in SPSS. The output from SPSS always uses two-sided p-values. This was worth an explanation, so I added a new question to the Ask Professor Mean page on how to do this.
There is a fierce debate about when you should use one-sided tests. In an editorial in the Journal of Clinical Epidemiology [Medline],† Knotttnerus and Bouter argue in favor of one-sided tests when it is obvious that a statistically significant change in the opposite direction would not influence practice any differently than a finding of no statistical difference. The sample size for a one sided test is smaller and this places a smaller burden on research subjects. Typically this would be when one approach is more expensive, more risky, or otherwise more troublesome. A finding of no difference between a new surgical approach an existing non-surgical approach, would lead to a recommendation for the non-surgical approach because it is less invasive. A comparison of coumarin and aspirin in preventing stroke would recommend aspirin if there were no demonstrated difference, because coumarin requires repeated blood testing. Knottnerus and Bouter also recommend one-sided tests when the comparison group is no treatment or an inactive placebo.
Lemuel Moye, in his fascinating book Statistical Reasoning in Medicine. The Intuitive P-Value Primer, argues forcefully against one-sided tests. Researchers should be open to the possibility that their proposed treatments could do more harm than good. Moye offers a compromise where the alpha level is allocated asymmetrically. For example, the "benefit" tail could be allocated .03 of† the total alpha level and the remaining .02 would be allocated to the "harm" tail.
Another example of where a one-sided test is called for is when the possibility of a change in the opposite direction would be immediately disregarded. One example where you might think that there is an obvious need for a one-tailed test is in the study of passive smoking. One writer did try to argue that passive smoking has a beneficial effect, as described in the 1998 BMJ article, The hot air on passive smoking [Medline], but most accept that if it has any effect at all, it would be harmful.
And yet, a federal judge vacated a U.S. Environmental Protection Agency report on passive smoking partly because of the use of one-sided tests. Judge Osteen ruled that
Finally, when an agency conducts activities under an act authorizing information collection and dissemination of findings, the agency has a duty to disseminate the findings made. EPA did not disclose in the record or in the Assessment: its inability to demonstrate a statistically significant relationship under normal methodology; the reasoning behind adopting a one-tailed test, or that only after adjusting the Agency's methodology could a weak relative risk be demonstrated. Instead of disclosing information, the Agency withheld significant portions of its findings and reasoning in striving to confirm its a priori hypothesis.
So apparently, Judge Osteen believes it is normal for an enforcement agency to examine the potential that some of the hazardous substances it evaluates might actually be helpful. In my opinion, it would be absurd for any scientist who claimed that exposure to passive smoke might have a protective effect against lung cancer. You can find the full text of Judge Osteen's ruling at the Junk Science web site. The EPA has a response to the general criticisms of its report on passive smoking (though not a direct response to Judge Osteen's ruling) on its web page.
Finally, there's a lot of controversy about whether p-values should be used at all. I'd like to write a web page about this sometime, but for now, here are some references and web pages.
- The Case Against Statistical Significance Testing. Carver RP. Harvard Educational Review 1978: 48(3); 378-399.
- The Earth Is Round (p < .05). Cohen J. American Psychologist 1994: 49(12); 997 - 1003.
- p Values, hypothesis tests, and likelihood: implications for epidemiology of a neglected historical debate. Goodman S. American Journal of Epidemiology 1993: 137(5); 485-95. [Medline]
- A Picture is Worth a Thousand p Values: On the Irrelevance of Hypothesis Testing in the Microcomputer Age. Loftus GR. Behavior Research Methods, Instruments & Computers 1993: 25(2); 250-256.
- Is statistical significance testing useful in interpreting data? Savitz DA. Reprod Toxicol 1993: 7(2); 95-100. [Medline]
- Sifting the evidence- what's wrong with significance tests? Sterne JAC, Smith GD. BMJ 2001: 322; 226-231. [Medline] [Full text] [PDF]
- Special Issue: Statistical Significance Testing. Roberts D, Penn State University. Accessed on 2003-03-20. roberts.ed.psu.edu/users/droberts/sigtest.htm
- Understanding P-values. Berger J, Duke University. Accessed on 2003-03-19. www.stat.duke.edu/~berger/p-values.html
- 326 Articles/Books Questioning the Indiscriminate Use of Statistical Hypothesis Tests in Observational Studies. Thompson WL. Accessed on 2003-03-19. www.cnr.colostate.edu/~anderson/thompson1.html
You can find an earlier version of this page on my original website.