Chapter 10 Glossary

placebo treatment: a fake treatment that we know has no effect, except through the power of suggestion. For example, in medical experiments, a participant may be given a pill that does not have a drug in it. By using placebo treatments, you may beable to make people “blind” to whether a participant is getting the real treatment. (p. 300)

 

single blind: when either the participant or the experimenter is unaware of whether the participant is getting the real treatment or a placebo treatment. Making the participant “blind” prevents the participant from biasing the results of the study; making the experimenter blind prevents the experimenter from biasing the results of the study. (p. 300)

 

double blind (double masked):  neither the participant nor the research assistant knows what type of treatment (placebo treatment or real treatment) the participant is getting. By making both the participant and the assistant “blind,” you reduce both subject (participant) and experimenter bias. (p. 300)

 

experimental group: the participants who are randomly assigned to get the treatment. (p. 285)

 

control group: the participants who are randomly assigned to not receive the treatment. The scores of these participants are compared to the scores of the experimental group to see if the treatment had an effect. (p. 285)

 

empty control group: a control group that does not receive any kind of treatment, not even a placebo treatment. One problem with an empty control group is that if the treatment group does better, we don’t know whether the difference is due to the treatment itself or to a placebo effect. To maximize construct validity, most researchers avoid using an empty control group. (p. 300)

 

independent variable: the treatment variable; the variable manipulated by the experimenter. The experimental group gets more of the independent variable than the controlgroup. Note: Don’t confuse independent variable with dependent variable. (p. 285)

 

levels of an independent variable: the treatment variable is often given in different amounts. These different amounts are called levels. (p. 285)

 

dependent variable (dependent measure): participants’ scores—the response that the researcher is measuring. In the simple experiment, the experimenter hypothesizes that the dependent variable will be affected by (depend on) the independent variable. (p. 288)

 

independently, independence: a key assumption of almost any statistical test. In the simple experiment, observations must be independent. That is, what one participant does should have no influence on what another participant does, and what happens to one participant should not influence what happens to another participant. Individually assigning participants to treatment or no-treatment condition and individually testing each participant are ways to achieve independence. (p. 284-285)

 

independent random assignment:  randomly determining, for each individual participant, and without regard to what group the previous participant was assigned to, whether that participant gets the treatment. For example, you might flip a coin for each participant to determine whether that participant receives the treatment. Independent random assignment to experimental condition is the cornerstone of the simple experiment. (p. 281)

 

experimental hypothesis: a prediction that the treatment will cause an effect. In other words, a prediction that the independent variable will have an effect on the dependent variable. (p. 282)

 

null hypothesis: the hypothesis that there is no treatment effect. Basically, this hypothesis states that any difference between the treatment and no-treatment groups is due to chance. This hypothesis can be disproven, but it cannot be proven. Often, disproving the null hypothesis lends support to the experimental hypothesis. (p. 283)

 

simple experiment: participants are independently and randomly assigned to one of two groups, usually to either a treatment group or to a no-treatment group. The simple experiment is the easiest way to establish that a treatment causes an effect. (p. 280)

 

internal validity: a study has internal validity if it can accurately determine whether an independent variable causes an effect. Only experimental designs have internal validity. (p. 280)

 

inferential statistics: the science of chance. More specifically, the science of inferring the characteristics of a population from a sample of that population. (p. 289)

 

populationthe entire group that you are interested in. You can estimate the characteristics of a population by taking large random samples from that population. (p. 302)

 

meanan average calculated by adding up all the scores and then dividing by the number of scores. (p. 303)

 

central limit theorem: the fact that, with large enough samples, the distribution of sample means will be normally distributed. Note that an assumption of the t test is that the distribution of sample means will be normally distributed. Therefore, to make sure they are meeting that assumption, many researchers try to have “large enough samples,” which they often interpret as at least 15participants per group. (p. 315)

 

t test: the most common way of analyzing data from a simple experiment. It involves computing a ratio between two things: (1) the difference between your group means; and (2) the standard error of the difference (an index of the degree to which group means could differ by chance alone).

       As a general rule, if the difference you observe is more than three times bigger than the standard error of the difference, then your results will probably be statistically significant. However, exact ratio that you need for statistical significance depends on your level of significance and on how many participants you have. You can find the exact ratio by looking at the t table in Appendix E and looking for where the column relating to your significance level meets the row relating to your degrees of freedom. (In the simple experiment, the degrees of freedom will be two less than the number of participants.) If the absolute value of the t you obtained from your experiment is bigger than the tabled value, then your results are significant. (p. 309)

 

statistical significance: when a statistical test says that the relationship we have observed is probably not due to chance alone, we say that the results are statistically significant. See also p <.05. (p. 289)

 

p < .05: in the simple experiment, p < .05 indicates that if the treatment had no effect, a difference between the groups at least as big as what was discovered would happen fewer than 5 times in 100. Since the chances of such a difference occurring by chance alone are so small, experimenters usually conclude that such a difference must be due, at least in part, to the treatment. (p. 310)

 

Type 1 error: rejecting the null hypothesis when it is really true. In other words, declaring a difference statistically significant when the difference is really due to chance. Thus, Type 1 errors lead to “false discoveries.” If you set p < .05, there is less than a 5% (.05) chance that you will make a Type 1 error. (p. 290)

 

Type 2 error: failure to reject the null hypothesis when it is really false; failing to declare that a difference is statistically significant, even though the treatment had an effect. Thus, Type 2 errors lead to failing to make discoveries. (p. 294)

 

powerthe ability to find differences; or, put another way, the ability to avoid making Type 2errors. (p. 294)

 

null results (nonsignificant results): results that fail to disprove the null hypothesis. Null results do not prove the null hypothesis because null results may be due to lack of power. Indeed, many null results are Type 2 errors. (p.290)