Hockenbury Discovering Psychology 5th txtbk

Inferential StatisticsA-13When researchers test for statistical significance, they usually employ statisticsother than z scores, and they may use distributions that differ in shape from the normalcurve. The logic, however, is the same. They compute some kind of inferentialstatistic that they compare to the appropriate distribution. This comparison tellsthem the likelihood of obtaining their results if chance alone is operating.The problem is that no test exists that will tell us for sure whether our interventionor manipulation “worked”; we always have to deal with probabilities, notcertainties. Researchers have developed some conventions to guide them in theirdecisions about whether or not their study results are statistically significant. Generally,when the probability of obtaining a particular result if random factors aloneare operating is less than .05 (5 chances out of 100), the results are considered statisticallysignificant. Researchers who want to be even more sure set their probabilityvalue at .01 (1 chance out of 100).Because researchers deal with probabilities, there is a small but real possibility oferroneously concluding that study results are significant; this is called a Type I error.The results of one study, therefore, should never be completely trusted. Forresearchers to have greater confidence in a particular effect or result, the studyshould be repeated, or replicated. If the same results are obtained in different studies,then we can be more certain that our conclusions about a particular interventionor effect are correct.There is a second decision error that can be made—a Type II error. This is whena researcher fails to find a significant effect, yet that significant effect really exists. AType II error results when a study does not have enough power; in a sense, the studyis not strong enough to find the effect the researcher is looking for. Higher powermay be achieved by improving the research design and measuring instruments, orby increasing the number of participants or subjects being studied.One final point about inferential statistics. Are the researchers interested only inthe changes that might have occurred in the small groups of people participatingin the health-promotion study, or do they really want to know whether the interventionswould be effective for people in general? This question focuses on thedifference between a population and a sample. A population is a complete set ofsomething—people, nonhuman animals, objects, or events. The researchers whodesigned this study wanted to know whether the interventions they developedwould benefit all people (or, more precisely, all people between the ages of 20 and56). Obviously, they could not conduct a study on this entire population. The bestthey could do was choose some portion of that population to serve as subjects; inother words, they selected a sample. The study was conducted on this sample. Theresearchers analyzed the sample results, using inferential statistics to make guessesabout what they would have found had they studied the entire population. Inferentialstatistics allow researchers to take the findings they obtain from a sample andapply them to a population.So what did the health-promotion study find? Did the interventions work? Theanswer is “yes,” sort of. The traditional- and alternative-treatment groups, whencombined, improved more than did the no-treatment control group. At the end ofthe study, participants in the two intervention programs had better self-perceptionsregarding health, better mood, more energy, and fewer physical symptoms. Comparedwith the traditional and the no-treatment groups, the alternative groupshowed greater improvement in health perceptions and a significant decrease indepression and the use of prescription drugs. Interestingly, participation in thetreatment groups did not generally result in changes in health risk, such as loweredblood pressure or decreased weight. The researchers believe that little changeoccurred because the people who volunteered for the study were basically healthyindividuals. The study needs to be replicated with a less healthy sample. In sum, theintervention programs had a greater effect on health perceptions and psychologicalvariables than on physical variables. The researchers concluded that a health-promotionregimen (either traditional or alternative) is helpful. I’m sure other studies will beconducted to explore these issues further!Type I errorErroneously concluding that study resultsare significant.Type II errorFailing to find a significant effect that does,in fact, exist.populationA complete set of something—people,nonhuman animals, objects, or events.sampleA subset of a population.