Studied by 236 people

5.0(6)

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probability

the chance or likelihood of some event occurring

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event

the outcome of a trial

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independent events

the occurrence of one has no effect on the probability of the occurrence of the other

example: how 2 people vote when they don't know each other

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dependent events

the outcome of one event is related to the outcome of another event

example: how 2 family members vote are often dependent events

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mutually exclusive

the occurrence of one precludes the occurrence of the other

example: tossing a coin will result in one of two events (heads or tails)

if you get heads, you will not get tails on said toss

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exhaustive

a set of events that represent all possible outcomes

example: heads, tails, and side in a coin toss

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Additive Law of Probability

the rule giving the probability of the occurrence of 2 or more mutually exclusive events

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Multiplicative Law of Probability

rule giving the probability of the joint occurrence of independent events

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Joint Probability

the probability of the co-occurrence (at the same time) of two or more events

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Conditional Probability

the probability of one event given the occurrence of some other event

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Risk

the number of occurrences on one event divided by the total number of occurrences of events

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risk ratio

the ratio of two risks

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Odds

The frequency of occurrence of one event divided by the frequency of occurrence of another event

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odds ratio

the ratio of two odds

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sampling error

variability of a statistic from sample to sample due to chance

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hypothesis testing

a process by which decisions are made concerning the value of parameters

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sampling distribution

the distribution of a statistic over repeated sampling

the statistic is usually the mean

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sampling distribution of the mean

the distribution of sample means over repeated sampling from one population

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standard error

the standard deviation of a sampling distribution

each sample in a sampling distribution has a standard deviation, but the whole distribution also has a standard deviation

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research hypothesis

the hypothesis that the experiment was designed to investigate

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alternative hypothesis (H1)

the hypothesis that is adopted when H0 is rejected, usually the same as the research hypothesis

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null hypothesis (H0)

the statistical hypothesis tested by the statistical procedure; usually a hypothesis of no difference or no relationship

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test statistic

the results of a statistical test

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significance level (rejection level)

the probability with which we are willing to reject H0 when it is in fact correct

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rejection region

the set of outcomes of an experiment that will lead to rejection of H0

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critical value

the value of a test statistic at or beyond which we will reject H0

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one-tailed test (directional test)

a test that rejects extreme scores in one specified tail of the distribution

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two-tailed test (non-directional)

a test that rejects extreme scores in either tail of the distribution

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type 1 error

the error of rejecting H0 when it is true

-far more common

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alpha (α)

the probability of a type 1 error

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type 2 error

the error of not rejecting H0 when it is false

-false negative

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beta (β)

the probability of a type 2 error

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power

the probability of correctly rejecting a false H0

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increasing the type 1 error rate

decreases the type 2 error rate

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increasing the type 2 error rate

decreases the type 1 error rate

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correlation

a measure of the relationship between variables

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correlation coefficient

same as correlation

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Pearson product-moment correlation coefficient (r)

most common kind of correlation coefficient

it ranges from -1 to +1

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scatterplot (scatter diagram/scattergram)

a figure in which the individual data points are plotted in two-dimensional space

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predictor variable

the variable from which a prediction is made

often the IV

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criterion variable

the variable to be predicted

often the DV

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the predictor (IV) generally goes on which axis?

the x axis

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the criterion (DV) goes on which axis?

the y axis

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regression line

the line of best fit drawn through a scatterplot

it is the line that minimizes the distance between all of the data points and itself

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if the regression line goes up from left to right on the scatterplot, the correlation coefficient is

positive

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if the regression line goes down from left to right on the scatterplot, the correlation coefficient is

negative

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the closer the data points are to the regression line,

the stronger the correlation

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the closer the correlation coefficient is to 0,

the weaker the correlation

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linear relationship

a situation in which the best-fitting regression line is a straight line

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curvilinear relationship

a situation that is best represented by something other than a straight line

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population correlation coefficient rho (ρ)

the correlation coefficient for the population

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degrees of freedom

the number of independent pieces of information remaining after estimating one or more parameters

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Spearman's correlation coefficient for ranked data (rs)

a correlation coefficient on ranked data

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range restrictions

refers to cases in which the range over which x or y values are artificially limited

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heterogeneous subsamples

data in which the sample of observations could be subdivided into two distinct sets on the basis of some other variable

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point-biserial correlation coefficient (r_pb)

the correlation coefficient when one of the variables is measured as a dichotomy

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dichotomous variables

Variables that can take on only two different values

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phi (Φ)

the correlation coefficient when both variables are measured as dichotomies

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regression

the prediction of one variable from knowledge of one or more other variables

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regression equation

the equation giving the regression line

ŷ=bx+a

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ŷ

predicted value of y

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b

the slope of the regression line

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a

the y intercept

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slope

amount of change in y for a one-unit change in x

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intercept

the value of y when x is 0

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regression coefficients

the general name given to the slope and the intercept (most often refers to slope)

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errors of prediction (residual)

The differences between Y and Ŷ

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least squares regression

regression of Y on the basis of X where we minimize the squared deviations of predicted scores from obtained scores

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to calculate slope

b= r (sY/sX)

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to calculate the intercept

a= ȳ- bx̄

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standard error of estimate

the average of the squared deviations about the regression line

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residual variance (error variance)

the square of the standard error of the estimate

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