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Table of Contents Exit Types of Statistics in Psychology Descriptive Statistics: Summarize numbers so they become more meaningful and easier to communicate to other people Inferential Statistics: Used for making decisions, for generalizing from small samples, and for drawing conclusions

Table of Contents Exit Graphical Statistics Presenting numbers pictorially (usually in a graph) so they are easier to visualize Subset of descriptive statistics Frequency Distribution: Table that divides an entire range of scores into a series of equal classes and then records the number of scores that fall into each class Histogram: Graph of a frequency distribution; scores are represented by vertical bars Frequency Polygon: Number of scores in each class is represented by points on a line

Table of Contents Exit Measures of Central Tendency A number that describes a typical score around which the other scores fall Mean: Add all the scores for each group and then divide by the total number of scores; one type of average Sensitive to extremely high or low scores in a distribution; not always the best measure of central tendency

Table of Contents Exit Measures of Central Tendency (cont.) Median: Arrange scores from highest to lowest and then select the score that falls in the middle; half the values fall above the median, and half fall below it Mode: Identifies the most frequently occurring score in a group Easy to obtain but often unreliable Main advantage: Gives the score actually obtained by the most people

Table of Contents Exit Measures of Variability Provide a single number that tell us how spread out the scores are Range: Difference between the highest and lowest scores Standard Deviation: Index of how much a typical score differs from the mean of a group of scores

Table of Contents Exit Standard Scores Z Score: Indicates how many standard deviations above or below the mean a score is Normal Curve: Bell-shaped curve, with a large number of scores in the middle and very few extremely high and low scores

Table of Contents Exit Fig. A.3 The normal curve. The normal curve is an idealized mathematical model. However, many measurements in psychology closely approximate a normal curve. The scales you see here show the relationship of standard deviations, z-scores, and other measures to the curve.

Table of Contents Exit Inferential Statistics Population: Entire set of subjects, objects, or events of interest (all married students in the United States) Impossible or impractical to obtain Samples: Smaller cross section of a population Easier and more practical (and cheaper!) to obtain More cost effective

Table of Contents Exit Inferential Statistics (cont.) Sample must be representative The membership and characteristics of the larger population must be reflected accurately Members of sample must be chosen randomly Each member of the population must have an equal chance of being selected for the sample Statistical Significance: Degree to which an event (results of an experiment, results of a drug trial) is unlikely to have occurred by chance alone

Table of Contents Exit Correlation Consistent, systematic relationship between two variables, measures, or events Scatter Diagram: Best way to visualize correlation; plots intersection of paired measures Positive Relationship: Increases in one measure (X) are matched by increases in the other (Y) The more cigarettes you smoke, the more likely you are to contract lung cancer

Table of Contents Exit Correlation (cont.) Zero Correlation: No relationship exists between two variables Relationship between hair color and intelligence test scores (IQs) Negative Relationship (or Correlation): As values of one measure increase (X), values in the other measure decrease (Y) The more alcohol you drink, the lower your coordination test scores will be

Table of Contents Exit Fig. A.5 Scatter diagrams showing various degrees of relationship for a positive, zero, and negative correlation. (Adapted from Pagano, 1981.)

Table of Contents Exit Coefficient of Correlation Statistical index ranging from –1.00 to +1.00; the sign indicates the direction of the relationship, and the number, the strength Perfect Positive Relationship: Correlation of +1.00 Perfect Negative Relationship: Correlation of – 1.00 Perfect correlations are rarely found in psychology It is statistically impossible to have a correlation coefficient greater than +1.00 or lesser than –1.00 Percent of Variance: Amount of variation in scores accounted for by the correlation

Table of Contents Exit Utility of Correlations Correlations help us identify relationships that are worth knowing Correlations are valuable for making predictions If a correlation exists, the two variables are related Correlation does NOT demonstrate causation! Many times a third, or perhaps an extraneous, variable could be creating the correlation