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Conclusions about the population are inferred from the information obtained about the sample Generalizations from the sample to the population Extrapolate findings from sample to population |
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Uses sample data to calculate a single number (statistic) to estimate the parameter of interest (for example a mean) |
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Point estimate does not include |
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information about the variability between samples (uncertainty) |
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Provides a range of reasonable values that are intended to include the parameter of interest Establishes degree of confidence |
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The range of values used in an interval estimation Influenced by the standard deviation and sample size |
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From the mean and standard deviation of a sample, we can predict the |
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intervals within which 68, 95 and 99% of our cases would be expected to fall |
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Acceptance or rejection of a hypothesis is based upon a |
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level of significance (alpha level) |
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Rejection of a null hypothesis when it in fact is true |
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What determines the probability of type 1 error? |
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If the null hypothesis is accepted when in fact it is false |
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Five tests of parametric data |
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Pearson correlation Independent Sample t-test Dependent Sample t-test One-way ANOVA Correlated F ratio |
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Five tests of non-parametric data |
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Spearman’s r
Chi-Square test
Mann-Whitney U test Kruskal-Wallis test Wilcoxon Signed test |
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Independent Sample t-test |
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Used to compare the average (MEAN) in one group with the average in another group |
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Compare the average for measurements made twice within the same person BEFORE and AFTER Used to compare a treatment group AND a matched control group |
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Used to compare more than two means from independent groups |
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Used in causal comparative studies Data are in frequency counts Compares observed and expected (theoretical) frequencies Typically used in a 2 X 2 table Chi square = sum [(observed-expected)^2/expected] |
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Non-parametric equivalent of independent t-test |
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Wilcoxon Signed-Rank Test |
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Nonparametric equivalent of the paired t-test |
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Non-parametric equivalent of One-way ANOVA |
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Three types of regression |
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Simple linear regression logistic regression Multiple regression |
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Used to assess how one continuous variable can be used to predict a second continuous outcome variable |
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Used to assess the predictive value of one or more variables on an outcome that is dichotomous |
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A multivariate technique that determines the correlation between a criterion variable (dependent variable) and a combination of two or more predictor variables (independent variables) An extension of simple linear regression to investigate the relationships among a number of different variables |
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