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A theorem that specifies the mean, standard deviation, and shape of the sampling distribution, given that the sample is large.
n>100 |
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| A method of sampling by which geographical units are randomly selected and all cases within each selected unit are tested. |
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| The Equal Probability of SElection Method for selecting samples. Every element of case in the population must have an equal probability of selection for the sample. |
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| The mean of a sampling distribution of sample means. |
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| The mean of a sampling distribution of sample proportions. |
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| Any population proportion |
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| The quality a sample is said to have if it reproduces the major characteristics of the population from which it was drawn. |
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The distribution of a statistic for all possible sample outcomes of a vertain size.
Under conditions specified in two theorems, the sampling distribution will be normal in shape, with a mean equal to the population value and a standard deviation equal to the population standard deviation divided by the square root of N. |
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| A method for choosing cases from a population by which every case and every combination of cases has an equal chance of being included. |
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| Standard error of the mean |
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| The standard deviation of a sampling distribution of sample means. |
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| A method of sampling by which cases are selected from sublists of the population. |
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| A method of sampling by which the first case from a list of the population is randomly selected. Thereafter, every kth case is selected. |
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| The probability of error, or the probability that a confidence interval does not contain the population value. Alpha levels are usually set at 0.10, 0.05 |
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| A criterion used to select sample statistics as estimators. A statistic is unbiased if the mean of its sampling distribution is equal to the population value of interest. |
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| An estimate of a population value in which a range of values is specified. |
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| A frequently used alternate way of expressing alpha, the probability that an interval estimate will not contain the population value. Confidence Levels of 90%, 95% correspond to alphas of 0.10 and 0.05. |
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| The extent to which the sample outcomes are clustered around the mean of the sampling distribution. |
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| An estimate of a population value where a single value is specified. |
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| The proportion of area under the sampling distribution that contains unlikely sample outcomes, given the null hypothesis is true, it is the probability of Type 1 error |
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| Critical Region (region of rejection) |
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| The area under the sampling distribution that, in advance of the test itself, is defined as including unlikely sample outcomes, given that the null hypothesis is true. |
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1-make assumptions
2-null and research hypothesis
3-draw picture
4-state alpha, df, and t or z critical
5- compute equation
6-findings |
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| Statistical tests that estimate the probability of sample outcomes if assumptions about the population (null hypothesis) are true. |
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| A statement of "no difference" in the context of single sample tests of significanece, the population from which the sample was drawn is assumed to have a certain characteristic or value. |
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| A type of hypothesis test used when 1- the direction of the difference can be predicted or 2- concern focuses on outcomes in only one tail of the sampling distribution |
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| Research Hypothesis (Hsub1) |
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| A statement that contradicts the null hypothesis. In the context of single sample tests of significance, the research hypothesis says the population from which the sample was drawn does not have a certain characteristic or value |
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| A distribution used to find the critical region for tests of sample means when σ is unknown and sample size is small. |
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| The t score that marks the beginning of the critical region of a t distribution |
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| The test statistic computed in step 5 of the six-step model. The sample outcome into either a t score or a z score. |
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| The value computed in step 5 of the 6 step model that converts the sample outcome into either a t or z score. |
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| a type of hypothesis test used when 1 the direction of the difference cannot be predicted or 2 concerns focus on outcomes in both tails of the sampling distribution. |
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Type 1 and Type 2 Errors
Alpha and Beta |
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Alpha Error- the probability of rejecting a null hypothesis that is, in fact, false.
Beta Error- The probability of failing to reject a null hypothesis that is, in fact, false. |
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| Independent Random Samples |
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| Random samples gathered in such a way that the selection of a particular case for one sample has no effect on the probability that any other particular case will be selected for the other samples. |
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| An estimate of the standard deviation of the sampling distribution of the difference in sample means based on the standard deviation of both samples. |
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| symbol for the standard deviation of the sampling distribution of the differences in sample proportions. |
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| symbol for the standard deviation of the sampling distribution of the differences in sample means |
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A test of significance appropriate for situations in which we are concerened with the differences among more than two sample means.
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| The test statistic computed in step 5 of the ANOVA test |
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| One-Way Analysis of Variance |
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| Applications of ANOVA in which the effect of a single independent variable on a dependent variable is observed. |
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| A technique for determining which pairs of means are significantly different |
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| Sum of Squares Between (SSB) |
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| The sum of the squared deviations of the sample means from the overall mean, weighted by sample size |
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| Sum of Squares Within (SSW) |
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| The sum of the squared deviations of scores from the category means |
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| Total Sum of Squares (SST) |
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| The sum of the squared deviations of teh scores from the overall mean. |
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