PSYCH 240 1st EditionFinal Exam Study Guide ChaptersSection 1: Variables -Statistics: mathematical tools for the organization, analysis, and interpretation of data-Descriptive statistics: summarize and describe a dataset-Inferential statistics: use data to make general conclusions (conclusions that go beyond the data at hand)-Variable: condition, characteristic, or measure that can take on different values-Value: one of the possible levels that a variable can take-Score: the value of a variable observed for a particular data-collection unit-Distribution: collection of scored across a number of data-collection units-Numeric/quantitative: values of the variable are numbers-Nominal/categorical/qualitative: values of the variable are verbal labels-Continuous: variable for which there are NO gaps in the values that the variable can take. There are an infinite number of possible values btw any two scores-Discrete: variable for which there ARE gaps in the values that the variable can take. Some scores simply never occur-Ratio Variable: equal-interval variable for which a value of zero truly indicates the complete absence of what is being measures-Equal Interval: equal-sized changes in the variable represent equal-sized changes in what is being measured-Ordinal: values of variable only indicate the rank of the score Section 2: Descriptives and Distributions -Central tendency: gives a number that describes the “typical” score for the variable (mean & median)-The mean is a misleading measure of central tendency for distributions with extreme skew and/or outliers. The median is preferred for skewed variables, since itis less sensitive to extreme scores.-For a positively skewed distribution, the mean will almost always be higher than the median-For a negatively skewed distribution, the mean will almost always be lower than the median-For symmetrical distributions, the mean and the median will have approximately thesame value-Variability: single number that summarizes how divergent the scored are from one another(range & variance) These notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.-Shape: graphs are used to visualize the distribution (histogram)-Histogram: graph that shows the frequency (number) of scored in a data set that equal a given value or fall in a given range of values- Symmetrical Distribution: if you draw a line in the middle of the range of scores, one side of the distribution looks very similar to the other side- Positively Skewed Distribution: if you draw a line in the middle of the range of scores, scores will be concentrated on the left side of the line, with fewer scores (a "tail") on the right side- Negatively Skewed Distribution: if you draw a line in he middle of the range of scored, scores will be concentrated on the right side of the line, with fewer scores (a "tail") on the left side- Floor Effects: many of the scores are concentrated near the lowest possible value for the variable. - Ceiling Effects: many of the scores are concentrated near the highest possible value for the variable- Bimodal Distribution: two peaks in the distribution with a clear dip in between- Uniform/Regular Distribution: distribution with an approximately equal number of scores across the entire range of valuesSection 3: Z-Scores -Z-scores: indicates the number of standard deviations that a score falls above or below the mean-z = (X=M)/S-X = zS + M-z=0: the score equals the mean, in other words it is very typical-Higher z’s: more atypically high scores-Lower z’s: more atypically low scores-Regardless of the original distribution, the z-score distribution will have a mean of 0,a standard deviation of 1, and the same shape as the original distribution Section 4: Correlation and Prediction -Formula of a Line: y-hat=a + b(x)-y-hat - predicted score for variable y-x - value of score on variable x-a - y-intercept or “regression constant” (predicted y value when x=0)-b - slope or “regression coefficient” (predicted chance in Y for every unit change in x)-Predictor Variable (x): what you use to predict Y-Criterion Variable (y): what you try to predict-Correlation Coefficient (r): a measure of the degree AND direction of relationship betweentwo variables-Sign (+ or -) indicated direction of the relationship-(+) means y increases as x decreases-(-) means y decreases as x increases-The absolute value indicates strength of correlation-0 is weakest correlation (no relationship)-1 or -1 is strongest correlation (perfect relationship) Section 5: Probability and Formal Distributions -Addition Rule: the probability of getting any one of multiple mutually-exclusive outcome is the sum of the individual probabilities of each outcome-Multiplication Rule: the probability of simultaneously observing multiple independent outcome is the product of the probabilities of each individual outcome-Percentage Change: the difference btw the new and old value of a variable divided by the old value (times 100 to make it a percentage)-PC = 100 X (NV-OV)/OV-% increase: NV = OV + (PC/100 X OV)-% decrease: NV = OV – (PC/100 X OV)-The same percent change can represent different absolute changes depending on theoriginal value-Statistic: index that is computed from the sample data-- regular letters-Parameter: characteristic of a population as a whole-- Greek letters-Law of Large Numbers: sample estimates will tend to converge to population values as sample size increases-If samples are not selected at random, sample statistics give biased estimates of population values. This is, the sample value tends to come out consistently lower or consistently higher than the population value Section 6: Hypothesis Testing- Null Hypothesis Significance Testing (NHST): we use this technique when we want to provide evidence against the null hypothesis and in factor of an alternative hypothesis- Specific Evidence: evidence for a hypothesis is specific if the evidence would be unlikely to be observed if the hypothesis was false- Comparison Population: a population of scores with a known distribution. The new sample that we are testing might or night mot belong to this population- Alpha: probability of concluding that we have good evidence for the alternative hypothesis when it is actually false (researchers typically use .05 or .01)o We use alpha to set the critical value(s)- Critical Value(s): the cutoff(s) that we will use to