scientists test theories

When scientists test theories, they do not try to prove them true. Theories can be supported based on the data collected, but obtaining support for something does not mean it is true in all instances. Proof of a theory is logically impossible. As an example, consider the following problem, adapted from Griggs and Cox (1982). This is known as the Drinking Age Problem (the reason for the name will become readily apparent).

On this task imagine that you are a police officer responsible for making sure the drinking-age rule is being followed. The four cards below represent information about four people sitting at a table. One side of a card indicates what the person is drinking and the other side of the card indicates the person’s age. The rule is: “If a person is drinking alcohol, then the person is 21 or over.” In order to check that the rule is true or false, which card or cards below would you turn over? Turn over only the card or cards that you need to check to be sure.

Does turning over the beer card and finding that the person is 21 years of age or older prove that the rule is always true? No—the fact that one person is following the rule does not mean that it is always true. How, then, do we test a hypothesis? We test a hypothesis by attempting to falsify or disconfirm it. If it cannot be falsified, then we say we have support for it. Which cards would you choose in an attempt to falsify the rule in the drinking age problem? If you identified the beer card as being able to falsify the rule, then you were correct. If we turn over the beer card and find that the individual is under 21 years of age, then the rule is false. Is there another card that could also falsify the rule? Yes, the 16 years of age card can. How? If we turn that card over and find that the individual is drinking alcohol, then the rule is false. These are the only two cards that can potentially falsify the rule. Thus, they are the only two cards that need to be turned over.

Even though disproof or disconfirmation is logically sound in terms of testing hypotheses, falsifying a hypothesis does not always mean that the hypothesis is false. Why? There may be design problems in the study, as described earlier. Thus, even when a theory is falsified, we need to be cautious in our interpretation. We do not want to completely discount a theory based on a single study.

alternative explanation (p. 6)

case study method (p. 4)

control (p. 8)

control group (p. 8)

correlational method (p. 5)

dependent variable (p. 7)

description (p. 3)

experimental group (p. 7)

experimental method (p. 6)

explanation (p. 3)

hypothesis (p. 2)

independent variable (p. 7)

negative relationship (p. 5)

observational method (p. 4)

population (p. 5)

positive relationship (p. 5)

prediction (p. 3)

quasi-experimental method (p. 6)

random assignment (p. 7)

sample (p. 5)

survey method (p. 4)

theory (p. 2)

variable (p. 2)

(Answers to odd-numbered questions appear in Appendix B.)

1. After describing your medical symptoms to your doctor, he claims he has a possible “theory” to explain your symptoms. What is wrong with his statement? How might he better state his beliefs?

2. Identify and briefly describe the three goals of science.

3. Identify advantages and disadvantages of naturalistic observation versus laboratory observation.

4. Identify the two predictive (relational) methods and describe each.

5. In a study of the effects of type of study on exam performance, participants are randomly assigned to one of two conditions. In one condition, participants study alone using notes they took during class lectures. In a second condition, participants study in interactive groups with notes from class lectures. The amount of time spent studying is held constant. All students then take the same exam on the material.

a. What is the independent variable in this study?

b. What is the dependent variable in this study?

c. Identify the control and experimental groups in this study.

d. Is the independent variable manipulated or a participant variable?

6. Researchers interested in the effects of caffeine on anxiety have randomly assigned participants to one of two conditions in a study, the no caffeine condition or the caffeine condition. After drinking two cups of either regular or decaffeinated coffee, participants will take an anxiety inventory.

a. What is the independent variable in this study?

b. What is the dependent variable in this study?

c. Identify the control and experimental groups in this study.

d. Is the independent variable manipulated or a participant variable?

7. Gerontologists interested in the effects of aging on reaction time have two groups of participants take a test in which they must indicate as quickly as possible whether a probe word was a member of a previous set of words. One group of participants is between the age of 25 and 45, whereas the other group of participants is between the age of 55 and 75. The time it takes to make the response is measured.

a. What is the independent variable in this study?

b. What is the dependent variable in this study?

c. Identify the control and experimental groups in this study.

d. Is the independent variable manipulated or a participant variable?

1. Jim is incorrect because he is inferring causation based on correlational evidence. He is assuming that because the two variables are correlated, one must be causing changes in the other. In addition, he is assuming the direction of the inferred causal relationship—that a lower income level causes psychological disorders, not that having a psychological disorder leads to a lower income level. The correlation simply indicates that these two

variables are related in an inverse manner. That is, those with psychological disorders also tend to have lower income levels.

2. a. The independent variable is exercise.

b. The dependent variable is life satisfaction.

c. The independent variable is a participant variable.

3. a. Naturalistic observation

b. Quasi-experimental method

c. Correlational method

d. Experimental method

• Explain and give examples of an operational definition.

• Explain the four properties of measurement and how they are related to the four scales of measurement.

• Explain the difference between a discrete variable and a continuous variable.

An important step when designing a study is to define the variables in your study. A second important step is to determine the level of measurement of the dependent variable, which will ultimately help to determine which statistics are appropriate for analyzing the data collected.

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