PSYC 243: Exam 3
41 Cards in this Set
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sampling distribution of the means
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frequency distribution of all possible sample means that occur when an infinite number of samples of the same size are randomly selected from one raw score population
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central limit theorem
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statistical principle that defines the mean, the standard deviation and the shape of the sampling distribution
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central limit theorem tells us
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1. forms an approximately normal distribution
2. mean is equal to the population mean
3.standard deviation is mathematically related to the standard deviation of the raw score population
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standard error of the mean
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standard deviation of the sampling distribution
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sampling error
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results when, by chance, the scores selected produce a sample statistic that is different from the population parameter it represents
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region of rejection
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contains means so unlikely to be representing the population that we reject them from representing that population
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criterion probability
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the probability that defines samples as too unlikely for us to accept as representing a particular population.
ex: .05
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critical value
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marks the inner edge of the region of rejection, defining the minimum value required for a sample to fall into the region of rejection
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parametric statistics
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procedures that require specific assumptions about the characteristics of the raw score population.
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2 assumptions of parametric statistics
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1. population of dependent scores forms a normal distribution
2.the scores are interval or ratio scale
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nonparametric statistics
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inferential procedures that dont require assumptions. nominal and ordinal data
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two tailed test
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used to predict a relationship when we do not predict the direction the scores will change
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one tailed test
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used to predict a relationship when we do predict the direction the scores will change
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statistical hypotheses
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describes the population parameters that the sample data represent if the predicted relationship does not exist
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alternative hypotheses
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describes the population parameters that the experiment does work as predicted.
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null hypothesis
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describes the population parameters that the sample data represent if the predicted relationship does NOT exist
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z test
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procedure for computing a z score for a sample mean on the sampling distribution of the means. USE WHEN SD IS KNOWN
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significant results
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indicates that our results are unlikely to occur if the predicted relationship does not exist in the population. Relationship is found
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nonsignificant results
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indicates that the results are likely to reflect chance sampling error without there being a relationship in nature
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Type I error
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rejecting the null hypothesis when it is true
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Type II error
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retaining the null hypothesis when it is false
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power
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probability of not making a type II error
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one sample t test
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parametric inferential procedure for a one sample experiment when the standard deviation of the raw score population must be estimated. DO NOT KNOW SD
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estimated standard error of the mean
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an estimate of the standard deviation of the sampling distribution
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point estimation
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describing a point on the variable at which the mean is expected to fall
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interval estimation
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specifying a range of values in which we expect the population parameter to fall
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confidence interval
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interval estimation that describes a range of values of the mean, one of which represents our sample mean. describes the highest and lowest values of the mean that are not significantly different than the sample mean
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independent samples t test
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parametric procedure for testing two sample means from independent samples from the same population
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homogeneity of variance
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the variances of the populations being represented are equal
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sampling distribution of differences between the means
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distribution of all possible differences between two means when they are drawn from the same population
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pooled variance
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weighted average of each variance
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standard error of the difference
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estimated standard deviation of the sampling distribution of differences between means
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confidence interval for the difference between two
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a range of differences between two means, any one of which is likely to be represented by the difference between two sample means
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related samples t test
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parametric procedure used with two related samples
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related samples
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each score in a sample is paired with a particular score in another sample
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matched samples design
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match each participant in one condition with another participant in the other condition
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repeated measures design
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each participant is tested under all conditions of the independent variabel
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sampling distribution of mean differences
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shows all possible values of the mean of D that occur when samples are drawn from a population of different scores
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confidence interval for the mean of D
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a range of values of the mean of D, any one of which our sample mean is likely to represent.
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effect size
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amount of influence changing the conditions of the independent variable had on dependent scores. Larger effect size= more important
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cohen's d
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measures effect size as the magnitude of the difference the conditions, relative to the population standard deviation
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PSYC 243: Test 3