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76 Cards in this Set
- Front
- Back
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population
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group of events/people a theory applies to
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sample
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people/events studied
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reliability
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how much the sample represents the population
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sample size
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as sample size increases, reliability increases
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experiment
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intervention, variables to assess
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independent variable
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manipulated by researcher, controlled
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dependent variable
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measured by researcher
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random selection
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equal chance of inclusion makes the experiment unbiased
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parameter
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characteristics of the population (ex: mean)
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statistic
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math function computed from data
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descriptive statistic
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summarizes aspect of data (ex: mean)
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estimator
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estimates parameter of population
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inferential statistic
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numerical value without physical meaning
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histogram
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height of each bar shows number of observations
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discrete variable
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usually integers, counts-people, classes, etc.
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continuous variable
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infinite set of values-height, iq, etc.
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nominal scale
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categories, labels, (no order, no number)
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ordinal scale
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ordered but no difference between the orders (1st, 2nd, 3rd)
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interval scale
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numbers, not zero, differences not meaningful
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ratio
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numbers including zero, differences meaningful
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mean
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sum of scores divided by number of scores
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median
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middle value
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mode
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most common value
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range
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distance from maximum to minimum
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variance
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distance from each datum to mean
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standard deviation
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difference between X and mean
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quantile
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value of X that is greater than certain part of data
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quartile
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1st=1/4 of data, 2nd (median), 3rd=3/4 of data
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percentile
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quantile defined by percentage
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cumulative distribution
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proportion of scores below or equal to given value
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z score
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number of standard deviations above or below mean
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standardized distribution
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distribution of z scores
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expected value
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mean
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replication
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doing same experiment with new sample
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sampling distribution
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probability distribution of some statistic over repeated replication
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central limit theorem
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distribution of sample mean
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standard error
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distance from M to Mu
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law of large numbers
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the larger the sample, the closer M will be to Mu
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hypothesis testing
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use inferential statistics to choose hypothesis
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null hypothesis
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no difference in means, means equal 0, nothing interesting in data
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alternate hypothesis
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there is a difference between the means, something is going on
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type 1 error
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null is correct but we reject it
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type 2 error
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null is false but we retain it
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alpha level
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cutoff for deciding between hypotheses, usually .05
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power
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if null is false, probability of correctly rejecting it
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critical region
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range of values that lead to p < alpha (rejecting null)
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critical value
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boundary of critical region
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binary data
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only yes/no, right wrong (counts)
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t statistic
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deviation of sample mean divided by estimated standard error
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t distribution
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sampling distribution of t statistic
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degrees of freedom
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tell you how many numbers are really being added
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independent samples t test
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assumes no relation between sample a and sample b, compare to mann-whitney test
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cohens d
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standardized effect size
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scatterplot
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relationship between x and y
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pearson correlation
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how close 2 variables are related
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independence
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knowing x tells nothing about y
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linear relationship
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correlation between x and y
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regression equation
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y^=b0+b1x1
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intercept
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value of y when all x's are 0
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f statistic
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if f is large enough, reject null
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anova
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analysis of variance, group differences
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grand mean
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mean of all scores and all groups
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repeated measures anova
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multiple measures for each subject, like friedman test
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factorial anova
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multiple independent variables
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goodness of fit
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deduction of data from prediction
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multinomial test
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count observations in every category
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expected frequency
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frequency of each category predicted by null
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marginal frequency
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total count for each category
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chi square statistic
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sum of squared z scores
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parametric statistic
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inferential statistics, assumptions about data
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nonparametric statistics
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less assumptions, ordinal scale, small sample size
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spearman correlation
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pearson correlation (ordinal, nonlinear)
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mann-whitney test
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independent t test, ordinal, small sample
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kruskal wallis test
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simple anova, ordinal, small sample
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wilcoxon test
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single/paired t test, ordinal, small sample
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friedman test
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repeated measures anova, ordinal, small sample
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