# Understanding Statistics in the Behavioural Sciences, 9th Edition - Chs. 1-8 - Definitions

## 79 terms

### Constant (p. 7)

A quantity whose value doesn't change. Pi(π) is an example. It has a value of 3.14159+ that never changes.

### Correlational studies (p. 10)

A scientific study in which a researcher investigates associations between variables.

### Data (p. 7)

The measurements that are made on the subjects of an experiment.

### Dependent variable (p. 7)

The variable in an experiment that an investigator measures to determine the effect of the independent variable.

### Descriptive statistics (p. 10)

Techniques that are used to describe or characterize the obtained sample data.

### Independent variable (p. 7)

The variable in an experiment that is systematically manipulated by an investigator.

### Inferential statistics (p. 10)

Techniques that use the obtained sample data to infer to populations.

### Method of authority (p. 4)

Something is considered true because of tradition or because some person of distinction says it is true.

### Method of intuition (p. 5)

Sudden insight, or clarifying idea that springs into consciousness, all at once as a whole.

### Method of rationalism (p. 4)

Uses reason alone to arrive at knowledge. It assumes that if the premises are sound and the reasoning is carried out correctly according to the rules of logic, then the conclusions will yield truth.

### Naturalistic observation research (p. 9)

A type of observational study in which the subjects of interest are observed in their natural setting. A goal of this research is to obtain an accurate description of behaviors of interest occurring in the natural setting.

### Observational studies (p. 9)

A type of research in which no variables are actively manipulated. The researcher observes and records the data of interest.

### Parameter (p. 7)

A number calculated on population data that quantifies a characteristic of the population.

### Parameter estimation research (p. 9)

A type of observational study in which the goal is to determine a characteristic of a population. An example might be the mean age of all psychology majors at your university.

### Population (p. 6)

The complete set of individuals, objects, or scores that an investigator is interested in studying.

### Sample (p. 6)

A subset of the population.

### Scientific method (p. 6)

The scientist has a hypothesis about some feature of realty that he or she wishes to test. An objective, observational study or experiment is carried out. The data is analyzed statistically, and conclusions are drawn either supporting or rejecting the hypothesis.

### Score

A particular person's value on a variable. Eg. Jack's score on self-esteem is 6.

### SPSS (p. 12)

Statistical Package for the Social Sciences

### Statistic (p. 7)

A number calculated on sample data that quantifies a characteristic of the sample.

### True experiment (p. 10)

In a true experiment, an independent variable is manipulated and its effect on some dependent variable is studied. Has the potential to determine causality.

### Value

A number or category that a variable can have. Eg. 6 is a value that a measure of self-esteem can have

### Variable (p. 7)

Any property or characteristic of some event, object, or person that may have different values at different times depending on the conditions.

### Continuous variable (p. 35)

A variable that theoretically can have an infinite number of values between adjacent units on the scale.

### Discrete variable (p. 35)

A variable for which no values are possible between adjacent units on the scale.

### Interval scale (p. 32)

A measuring scale that possesses the properties of magnitude and equal interval between adjacent units on the scale, but doesn't have an absolute zero point. Celsius scale of temperature measurement is a good example of an interval scale.

### Nominal scale (p. 31)

The scale is composed of categories, and the object is "measured" by determining to which category the object belongs. The categories comprise the units of the scale. An example would be brands of MP3 players; the units would be Apple, Microsoft, Sony, Creative Labs, etc.

### Ordinal scale (p. 32)

This is a rank-ordered scale in which the objects being measured are rank-ordered according to whether they possess more, less or the same amount of the variable being measured. An example is ranking Division 1 NCAA collage football teams according to which college or university football team is considered the best, the next best, the next next best, and so on.

### Ratio scale (p. 33)

A measuring scale that possesses the properties of magnitude, equal intervals between adjacent units on the scale, and also possesses an absolute zero point. The Kelvin scale of temperature measurement is an example of a ratio scale.

### Real limits of a continuous variable (p. 35)

Those values that are above and below the recorded value by one-half of the smallest measuring unit of the scale.

### Summation (p. 27)

Operation very often performed in statistics in which all or parts of a set (or sets) of scores are added.

### Bar graph (p. 58)

Graph of nominal or ordinal data, where a bar is drawn for each category and the height of the bar represents the frequency or number of members of that category.

### Bell-shaped curve (p. 62)

Frequency graph named "bell-shaped" because it looks like a bell.

### Cumulative frequency distribution (p. 49)

The number of scores that fall below the upper real limit of each interval.

### Cumulative percentage distribution (p. 49)

The percentage of scores that fall below the upper real limit of each interval.

### Exploratory data analysis (p. 62)

A recently developed technique that employs easily constructed diagrams that are useful in summarizing and describing sample data.

### Frequency distribution (p. 43)

A listing of score values and their frequency of occurrence.

### Frequency distribution of grouped scores (p. 44)

Graph that is used with interval or ratio data. Identical to a histogram, except that instead of using bars, the midpoints of each interval are plotted and joined together with straight lines, and the lines extended to meet the horizontal axis at the midpoint of the intervals that are immediately beyond the lowest and highest intervals.

### Frequency polygon (p. 58)

Graph that is used with interval or ratio data. Identical to a histogram, except that instead of using bars, the midpoints of each interval are plotted and joined together with straight lines, and the lines extended to meet the horizontal axis at the midpoint of the intervals that are immediately beyond the lowest and highest intervals.

### Histogram (p. 58)

Similar to a bar graph, except that it is used with interval or ratio data. Class intervals are plotted on the horizontal axis, a bar is drawn over each class interval such that each class bar begins and ends at the real limits of the interval. The height of each bar corresponds to the frequency of the interval and the vertical bars touch each other rather than spaced apart as with the bar graph.

### J-shaped curve (p. 62)

Frequency graph named J-shaped because it has the shape of the letter "J."

### Negatively skewed curve (p. 62)

A curve on which most of the scores occur at the higher values, and the curve tails off toward the lower end of the horizontal axis.

### Percentile point (p. 51)

The value on the measurement scale below which a specified percentage of the scores in the distribution fall.

### Percentile rank (p. 54)

The percentage of scores with values lower than the score in question.

### Positively skewed curve (p. 62)

A curve on which most of the scores occur at the lower values, and the curve tails off toward the higher end of the horizontal axis.

### Relative frequency distribution (p. 49)

The proportion of the total number of scores that occur in each interval.

### Skewed curve (p. 60)

A curve whose two sides do not coincide if the curve is folded in half; that is, a curve that is not symmetrical.

### Stem and leaf diagrams (p. 62)

An alternative to the histogram, that is used in exploratory data analysis. A picture is shown of each score divided into a stem and leaf, separated by a vertical line. The leaf for each score is usually the last digit, and the stem is the remaining digits. Occasionally, the leaf is the last two digits depending on the range of the scores. The stem is placed to the left of the vertical line, and the leaf to the right of the line. Stems are placed vertically down the page,and leafs are placed in order horizontally across the page.

### Symmetrical curve (p. 60)

A curve whose two sides coincide if the curve is folded in half.

### U-shaped curve (p. 62)

Frequency graph named U-shaped because it has the shape of the letter "U."

### X axis (abscissa) (p. 56)

The horizontal axis of a graph.

### Y axis (ordinate) (p. 56)

The vertical axis of a graph.

### Arithmetic mean (p. 70)

The sum of the scores divided by the number of scores.

### Central tendency (p. 70)

The average, middle, or most frequent value of a set of scores

### Deviation score (p. 79)

The distance of the raw score from the mean of its distribution.

### Dispersion (p. 79)

The spread of a set of scores.

### Median (p. 75)

The scale value below which 50% of the scores fall.

### Mode (p. 77)

The most frequent score in the distribution.

### Overall mean (p. 73)

Sometimes called weighted mean. The average value of several sets or groups of scores. It takes into account the number of scores in each group and in effect, weights the mean of each group by the number of scores in the group.

### Range (p. 79)

The difference between the highest and lowest scores in the distribution.

### Standard deviation (p. 79)

A measure of variability that gives the average deviation of a set of scores about the mean.

### Variability (p. 70)

Refers to the spread of a set of scores.

### Variance (p. 85)

The standard deviation squared.

### Z score (standard score) (p. 98)

A transformed score that designates how many standard deviation units the corresponding raw score is above or below the mean.

### Asymptotic (p. 96)

Approaching a given value as a function extends to infinity. For the normal curve, it refers to how the Y value of the normal curve approaches 0 (the X axis) as X extends to and infinity. Y gets closer and closer to 0, but never quite reaches it.

### Normal curve (p. 96)

A symmetrical, bell shaped curve with mean, median, and mode equal to each other, and specified kurtosis. Kurtosis refers to the sharpness or flatness of a curve as it reaches its peak.

Gives the probability of occurrence of one of several events. If there are only two events, A and B, the addition rule gives the probability of occurrence of A or B. In equation form, p(A or B)= p(A) + p(B) - p(A and B).

### A posteriori probability (p. 184)

Probability determined after the fact, after some data has been collected. In equation form, p(A) = Number of times A has occurred/Total number of occurrences.

### A priori probability (p. 184)

Probability determined without collecting any data; deduced from reason alone. In equation form, p(A) = Number of events classifiable as A/Total number of possible events.

### Exhaustive set of events (p. 190)

A set that includes all of the possible events.

### Independence of two events (p. 191)

The occurrence of one event has no effect on the probability of occurrence of the other.

### Multiplication rule (p. 191)

Gives the probability of joint or successive occurrence of several events. If there are only two events, the multiplication rule gives the probability of occurrence of A and B. In equation form, p(A and B) = p(A)p(B|A).

### Mutually exclusive events (p. 186)

Two events that cannot occur together; that is, the occurrence of one precludes the occurrence of the other.

### Probability (p. 184)

Expressed as a fraction or decimal number, probability is fundamentally a proportion; it gives the chances that an event will or will not occur.

### Probability of occurrence of A or B (p. 186)

The probability of occurrence of A plus the probability of occurrence of B minus the probability of occurrence of both A and B.

### Probability of occurrence of both A and B (p. 191)

The probability of occurrence of A times the probability of occurrence of B given that A has occurred.

### Random sample (p. 180)

A sample selected from the population by a process that ensures that (1) each possible sample of a given size has an equal chance of being selected and (2) all the members of the population have an equal chance of being selected into the sample.

### Sampling with replacement (p. 183)

A method of sampling in which each member of the population selected for the sample is returned to the population before the next member is selected.

### Sampling without replacement (p. 183)

A method of sampling in which the members of the sample are not returned to the population before selecting subsequent members.