Abnormal Psychology. William J. Ray

deviation) match what we expect to be the same measures in the larger population.

      Inferential statistics might be used to study how a group of individuals with depression respond to mindfulness meditation.

      © iStockphoto.com/Tassii

      Conceptually, we ask if our control and experimental groups could be considered equal before any treatment (IV) was introduced. That is, would we expect them to be drawn equally from the larger population? One way in which we seek to make our experimental and control groups equal is through random assignment to the groups. A critical question that is asked in terms of empirically supported treatments (see Chapter 1) is whether the participants were randomly assigned to the treatment condition. If so, then we take the support for a particular treatment as being more valid.

      probability: the likelihood that a set of results in an experiment differed from what would be expected by chance

      inferential statistics: a method of analysis that concerns the relationship between the statistical characteristics of the population and those of the experimental sample

      sample: participants in a study

      Given a group of potential subjects, we could expect some of them to be motivated to be part of the experiment, others to be tired, some to be more intelligent, some to have faster reaction times than others, and so forth. By randomly assigning these individuals to groups, we would expect to make the two groups equal.

      Another part of the statistical treatment of the null hypothesis is related to probability. If you were to toss a coin a large number of times, you would expect to have an equal number of heads and tails. The idea of no differences forms the basis of the null hypothesis, which was developed by Sir Ronald Fisher (1935). He sought to determine whether a set of results differed from what would be expected.

      What we need, of course, is a technique for determining if a set of results is different from what would be expected. One of the common statistical techniques used for this is called the t test. It was actually developed near the beginning of the last century by William Gosset, who worked for the Guinness Brewery in Dublin. Gosset wanted a way of knowing if all the batches of beer were the same. In this case, he actually wanted the null hypothesis to be true. Fisher developed the F test, which is conceptually similar to the t test. In fact, mathematically, t² = F.

      In our experiment, we can think of the t test or F test, asking the question of what is the difference in reaction time between the experimental and control groups. The larger the difference, the more certain we can be that the IV had an effect. In the end, we are never fully certain that our results are or are not due to chance. Instead, we use statistics to help us to make a best guess by assigning a probability to the statement that our results are not due to chance alone. That is, we may say that results from our study could have happened by chance less than 1 time out of every 100. Said in other words, if we ran the same study 100 times, each with a different set of subjects drawn from the total population, what are the odds we would not obtain the same results?

      Confound Hypothesis

      The second question asked is whether our results are due to a confound rather than the IV. What is a confound? Almost anything can be a confound. A confound is something that systematically biases the results of our research.

      confound: a factor that systematically biases the results of experimental research

      It may be the fact that women are more likely to go to their mental health provider than men. Thus, a study that looks at rates of a particular disorder based on provider reports may be biased in terms of a gender difference. Since the amount of sunlight can influence the experience of depression, a confound may be introduced into a depression experiment if one group is studied in the winter when there is less light and another group is studied in the summer when there is more sunshine. A confound may also be introduced when one group is made up of more men than women if the disorder under study shows gender differences. In one treatment study, the control group was instructed by young, inexperienced technicians, whereas the experimental group was instructed by an older, more experienced professional. This difference may have produced a confound in the results.

      Some confounds can be prevented or controlled. However, other factors can never be controlled. You cannot control world events, but you can ask whether there is any reason to believe that a particular event that took place inside or outside of the laboratory could have influenced one group more than another and thus introduced a confound.

      Research Hypothesis

      After ruling out the null hypothesis and the confound hypothesis, we can assume that the results reflect the action of the IV. Our next step is to consider what this means. We begin to generalize from our set of data and consider both the implications of our results for other groups of people and the theoretical implications of the data. Sometimes we are led to new ideas, which in turn generate new research hypotheses, which can be investigated with additional experiments.

      Figure 3.8 Four Major Steps in the Experimentation Process

      Figure 3.8 presents a simplified outline of this procedure, which reflects the evolutionary nature of science. The steps include (1) the development of the hypothesis, (2) the translation of this hypothesis into a research design, (3) the running of the experiment, and (4) the interpretation of the results. You will notice that there is also an arrow from Step 4 back to Step 1. Researchers take the results and interpretations of their studies and create new research studies that refine the previous hypotheses.

      In psychological research, we have some powerful techniques to help us achieve this goal. Unlike the detective who must always reconstruct events after the fact, the researcher has the advantage of being able to create a new situation in which to test ideas. This is comparable to a homicide detective’s being able to bring a dead man back to life and place him in the presence of each suspect until the murder is reenacted. Such a reenactment might lack suspense and not make it in prime time, but it would increase the certainty of knowing who committed the murder.

      Increased certainty is a large part of the experimental process. Scientists increase certainty by creating an artificial situation—the experiment—in which important factors can be controlled and manipulated. Through control and manipulation, participant variables may be examined in detail, and the influence of one variable on another may be determined with certainty. The Cultural LENS below describes randomized controlled trials (RCTs), examining treatments for mental illness in low- and middle-income countries.

      Cultural Lens

      Randomized Controlled Trials of Global Mental Health Treatments in Low- and Middle-Income Countries

      A mental health worker meets with patients and their families.

      The Sydney Morning Herald/Contributor/Fairfax Media/Getty Images

      There is a large difference in mental health treatment in high-income countries like the United States, England, Germany, France, Japan, and Australia as compared with low- and middle-income countries such as India, Pakistan, China, Chile, Mexico, and many countries in Africa. In high-income countries, there are a larger number of professionals who can deliver mental health services. Treatment procedures such as medications and psychological therapies have also been developed and tested in these higher-income countries. In contrast, there are fewer mental