The Handy Psychology Answer Book. Lisa J. Cohen

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      Prisoners at the Buchenwald concentration camp in Germany during World War II line up for roll call. In response to the Nazis’ horrifically brutal experiments on their prisoners, the Nuremberg Code, a set of ethical requirements regarding human research, was developed in 1947.

      How do the methods of the social sciences differ from those of the hard sciences?

      In general, the role of math is different in the social sciences than in the hard sciences. In the hard sciences, especially physics, mathematics is used to identify fixed laws of nature. Once a mathematical equation is identified to explain the behavior of an object, the equation can be used to predict the behavior of the object with extraordinary precision. Consider the equations that send rockets into space. Thus, mathematics in the hard sciences is predictive and deterministic. All of the object’s behavior can be predicted by the equation.

      Aspect of modern physics, such as quantum mechanics and Heisenberg’s Uncertainty Principle, do contradict this certainty, however, although only in the realm of the extremely small (e.g. the sub-atomic particles) or the extremely large. In psychology, the topics of study are so complex that it is not possible to predict all human behavior with mathematical equations. Whether that will ever be possible is debatable, but it certainly has not been done yet. What does that mean for the role of mathematics in psychological science? In psychology, mathematics is probabilistic. We estimate the likelihood that certain statements are true or not. Further, these estimates are based on the aggregate, on the behavior of groups. Therefore, while we may be able to say a lot about the likely behavior of groups, we are unable to predict the behavior of any given individual with certainty. For example, based on samples showing increased beer drinking among male college students compared to females, we can predict that, in general, male college students will drink more beer than females but we cannot predict the behavior of any given college student.

      Why is sample selection important in psychological research?

      In psychological research, we try to draw conclusions about a larger population from observations of a small sample. We cannot study all male college students or all people with schizophrenia so we study a sample of the population of interest and then try to apply our findings to the larger population. For this reason it is critical to make sure the sample is similar to the larger population. There are many ways the sample can vary from the larger population. The way we recruit our study subjects may bias the sample right from the start. For example, if you want to study illegal behavior, you are likely to find your sample in the judicial system. Right off the bat, your sample is biased toward people who have been arrested, leaving out the people who never got caught. If you want to study people with depression, you are likely to study people in the mental health system and your sample will be biased toward people who seek treatment. Because it is virtually impossible to remove all problems from sample selection, researchers must carefully describe their samples so that the applicability to a larger population, or the study’s generalizability, can be assessed.

      What are statistics and how do they work?

      Millions of psychology majors grit their teeth and roll their eyes at the very thought of statistics. Nonetheless, statistics are a fundamental part of psychological research. Statistics provide a mathematical technique to measure the relationships between two or more variables (traits of interest such as intelligence, aggression, or severity of depression). Statistics can show how these variables relate. It can show the strength of their relationship and the probability that the relationships found in a given study are likely to be true findings, rather than a statistical fluke, i.e., due to chance. The most common statistics are measures of central tendencies, specifically: the mean, median, and the mode; measures of group differences such as the t-test, analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA); and measures of covariation, such as correlation, factor analyses, and regression analyses. Measures of covariation assess the degree to which two or more variables change in relation to each other. For example, height and weight covary (or are correlated) while age and ethnicity do not. In general tall people weigh more than shorter people while ethnicity does not change with age.