Helpful Psychology/Statistical Terms
This section is based on the glossary in Keppel, Saufley and Tokunaga, Introduction to Design and Analysis

Alternative Hypothesis: The hypothesis that is accepted if the null hypothesis is rejected, usually represented by the symbol H1. Also known as the experimental or research hypothesis. The alternative hypothesis usually states that the independent variable has had an effect on the dependent variable that cannot be explained by chance alone. ( N.B. You never prove that the alternative hypothesis is correct, even if the null hypothesis is rejected, there is always a chance that you have wrongly rejected it.)

Between subjects design: An experimental design in which each subjects is randomly assigned to only one of the treatment conditions.

Case-study: A research method involving the detailed study of a single individual, used mainly in clinical psychology and neuropsychology.

Control Group: A group of subjects which does not receive the experimental treatment but in all other respects is treated in the same way as the experimental group, (so as to tease out the effects of the treatment itself). In medical studies involving the administration of drugs the control group is known as the placebo group. A neutral substance ( placebo ) is administered to this group without the subjects knowing if it is an active drug or not.

Correlational design: A type of research design in which patterns of correlations are analyzed.

Dependent Variable: The selected behavior, which is measured to try to gauge the effect of the independent variable in an experimental design.

Descriptive Statistics: Data summarized in numerical form, such as mean, median, mode. This forms the first stage of data analysis. Means, standard deviations and standard errors are presented in the form of a table.

External Validity: The degree to which results of a study with a sample of subjects can be generalized to make statements about a much larger population of subjects.

F-ratio: A statistical index relating systematic variation in the data (caused by treatment effects plus random error) to unsystematic variability in the data (caused by random error alone). The effects of treatments plus error is the numerator and the effect of error (chance) is the denominator of the F-ratio

Independent Variable: The variable manipulated by the experimenter. It is a feature of a task given to subjects, or a manipulation of the external or internal environment. Internal environment refers to attitudes, beliefs etc.

Inferential Statistics: Procedures and measures used to make inferences about population characteristics from samples drawn from that population. The process of hypothesis testing is part of inferential statistics.

Longitudinal Study: A form of research often used to study, e.g., developmental issues where the group of subjects is studied over an extended period of time. Measurements are taken several times at regular intervals to look at the effect of time on the dependent variable.

Matched-Subjects Designs: A class of between-subjects design in which the subjects are matched on one or more relevant characteristics. This design is used to reduce between groups variability.

Mean: A measure of central tendency, giving an average of a set of scores (i.e. the sum of all the scores divided by the number of scores in the set).

Median: Measure of central tendency, giving the value of the middlemost score (above or below which half of all the scores lie). If there is an even number of scores the median is the average of the two middle scores.

Naturalistic Observation: A form of observational research in which the observer records information about naturally occurring behavior while attempting not to intervene or affect the behavior in any way. This research is also described as unobtrusive.

Nominal scale: Data is allocated into different (often named or numbered) categories. For example, the allocation of books in a library catalogue to different topics. Data on this scale cannot be meaningfully added and subtracted.

Null hypothesis: This is usually a statement of "no effect", that is to say that the independent variable will not have any effect on the dependent variable and that any differences between the experimental and control groups are attributable to chance. The null hypothesis is usually represented by the symbol H0, and is stated in order that it can be rejected as an explanation for the results of the experiment.

Observational Research: The systematic study of behavior as it occurs in the natural environment.

Ordinal scale: A scale of measurement where data are put in order, but where there is no fixed amount of difference between the points on the scale. For example, the rank order of premier league football teams, or world ranking of tennis players.

Placebo effect: A positive or therapeutic benefit resulting from the administration of a placebo to someone who believes the treatment is real.

Random sampling: A procedure in which each member of the population has an equal chance of being included in the sample.

Reliability: The consistency with which a measuring instrument (such as a psychometric test) performs its' function, gauged, for example, by comparing test scores from the same subjects at different times.

Significance (statistical): Is achieved when there is a low probability that the results of an experiment occurred by chance alone. In psychology it is conventional that results are said to be significant if the probability of their occurrence by chance is equal to or less than 5 per cent or 0.05

Significance Level: The probability with which the experimenter is willing to reject the null hypothesis ( in favor of the alternative hypothesis ) when the null hypothesis is in fact correct. Also known as the probability of a type I error.

Standard Deviation: A measure of dispersion within a set of data, calculated from the square root of the variance, to give a value in the same range as raw scores. The standard deviation is the spread of scores around the mean of the sample.

Standard Error: The standard deviation of the sampling distribution of the mean. A statistical estimate of the population standard deviation based on the mean and standard deviation of one sample. Calculated by dividing the standard deviation of the sample by the square root of the number of subjects in the sample.
t-test: A parametric statistical test of the difference between the means of two samples.

Type I Error: An error of statistical inference when null hypothesis is rejected when it is true. This is an error of "seeing too much in the data."

Type II Error: An error of statistical inference when the null hypothesis is retained when it is false. This is an error of "not seeing enough in the data."

Validity: From the Latin validus, (strong), the degree to which a measuring instrument measures what it is supposed to measure.

Variability: The degree to which differences exist among a set of scores. The standard deviation is usually used to describe the variability of scores in a sample.

Variable: A property that can take different values. In research designs variables are classed as independent and dependent.

Within-Groups Variability: A measure of variability based on the variation of subjects treated alike in an experiment, ( i.e. the subjects are in the same group ). The amount of within-groups variability gives a measure of experimental error.

Within-Subjects Design: An experimental design where all subjects receive all treatment conditions. Also called a repeated measures design.

Z-score: A score expressed in units of standard deviations from the mean. Also known as a standard score

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