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procedure for deciding whether the outcome of a study (results for a sample) support a particular theory or practical innovation (which is thought to apply to a population).
prediction, often based on informal observation, previous research, or theory that is tested in a research study
Set of principles that attempt to explain one or more facts, relationships, or events;
**psychologists often derive specific predictions from theories that are then tested in research studies
statement in hypothesis testing about the predicted relation between population (often a prediction of a difference between population means)
Statement about a relation between populations that is the opposite of the research hypothesis;
Statement that in the population there is no difference (or a difference opposite to that predicted) between populations;
Contrived statement set up to examine whether it can be rejected as part of hypothesis testing
distribution used in hypothesis testing. It represents the population situation if the null hypothesis is true. It is the distribution to which you compare the score based on your sample's results
Cutoff Sample Score
point in hypothesis testing, on the comparison distribution at which, if reached or exceeded by the sample score, you reject the null hypothesis.
*aka- critical value
Conventional levels or significance
(p < .05 < .01)
*levels of significance widely used in psychology
Conclusion that the results of a study would be unlikely if in fact the sample studied represents a population that is no different from the population in general; an outcome oh hypothesis testing in which the null hypothesis testing in which the null hypothesis is rejected
Research hypothesis predicting a particular direction of difference between populations.
**example- a prediction that the population like the sample studied has a higher mean than the population in general
Hypothesis-testing procedure for a directional hypothesis; situation in which the region of the comparison distribution in which the null hypothesis would be rejected is all on one side (tail) of the distribution.
research hypothesis that does not predict a particular direction of difference between the population like the sample studied and the population in general
Hypothesis-testing procedure for a nondirectional hypothesis; the situation in which the region of the comparison distribution in which the null hypothesis would be rejected is divided between the two sides (tails) of the distribution
Distribution of Means
distribution of means of samples of a given size from a population; comparison distribution when testing hypothesis involving a single sample of more than one individual.
**The mean of a distribution of means is equal to the mean of the population of individuals
Variance of a distribution of means
variance of the population divided by the number of scores in each sample
MEAN of a Distribution of means
the mean of a distribution of means of samples of a given size from a population; the same as the mean of the population of individuals
Standard deviation of a distribution of means
square root of the variance of a distribution of means
Standard error of the mean
same as standard deviation of a distribution of means.
hypothesis-testing procedure in which there is a single sample and the population variance is known
roughly, the range of scores (that is the scores between an upper and lower value) that is likely to include the true population mean; more precisely, the range of possible population means from which it is not highly unlikely that you could have obtained your sample mean
incorrect conclusion in hypothesis testing in relation to the real (but unknown) situation, such as deciding the null hypothesis is false when it is really true
Type I Error
Rejecting the Null hypothesis when in fact it is true; getting a statistically significant result when in fact the research hypothesis is not true
probability of making a Type I error
**(same as significance level)
Type II Error
Failing to reject the Null hypothesis when in fact it is false; failing to get a statistically significant result when in fact the research hypothesis is true.
statistical method for combining effect sizes from different studies
probability that the study will give a significant result if the research hypothesis is true
Hypothesis-testing procedure in which the population variance is unknown; it compares t scores from a sample to a comparison distribution called a t distribution
t test for a single sample
hypothesis testing procedure in which a sample mean is being compared to a known population mean and the population variance is unknown
estimate of a population parameter that is likely systematically to overestimate or underestimate the true value of the population parameter.
**example- SD2 would be a biased estimate of the population variance (it would systematically underestimate it).
Unbiased estimate of the population variance (S2)
estimate of the population variance, based on sample scores, which has been corrected so that it is equally likely to overestimate or underestimate the true population variance
*the correction used is dividing the sum of squared deviations by the sample size minus 1, instead of the usual procedure of dividing by the sample size directly
Degrees of Freedom (df)
number of scores free to vary when estimating a population parameter; usually part of a formula for making that estimate
**example- in the formula for estimating the population variance from a single sample, the degrees of freedom is the number of scores minus 1
mathematically defined curve that is the comparison distribution used in a t test
table of cutoff scores on the t distribution for various degrees of freedom, significance levels, and one-and-two-tailed tests
on a t distribution, number of standard deviations from the mean (like a Z score, but on a t distribution).
t- test for independent means
hypothesis-testing procedure in which there are two separate groups of people tested and in which the population variance is not known
Distribution of differences between means
distribution of differences between means of pairs of samples such that for each pair of means, one is from one population and the other is from a second population; the comparison distribution in a t-test for independent means
association between scores on two variables
graph showing the relationship between two variables: the values of one variable are along the horizontal axis and the values of the other variable are along the vertical axis; each score is shown as a dot in this two-demensional space.
relation between two variables that shows up on a scatter diagram as the dots roughly following a straight line.
relation between two variables that shows up on a scatter diagram as dots following a systematic pattern that is not a straight line.
no systematic relationship between two variables
relation between two variables in which high scores on one go with high scores on the other, mediums with mediums, and lows with lows;
**on a scatter diagram the dots roughly follow a straight line sloping up and to the right
relation between two variables in which high scores on one go with low scores on the other, mediums with mediums and lows with highs
** on a scatter diagram the dots roughly follow a straight line sloping down and to the right
Product of deviation scores
the result of multiplying the deviation score on one variable by the deviation score on another variable
Correlation Coefficient (r)
measure of degree of linear correlation between two variables ranging from -1 (a perfect negative linear correlation) through 0 (no correlation) to +1 (a perfect positive correlation).
Direction of causality
path of causal effect; if X is thought to cause Y then the direction of causality if from X to Y
Proportionate reduction in error (r2)
measure of association between variables that is used when comparing associations
scores with an extreme (very high or very low) value in relation to the other scores in the distribution
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