BST 322 Week 2 questions and answers (verified for accuracy)

Sampling error (SEM) The difference between the sample value and the parameter. Error in a statistical analysis arising from the unrepresentativeness of the sample taken. Reflects the tendency for statistics to fluctuate from one sample to another. Sampling distribution Are actually probability distributions, is central to estimates of sampling error. Sampling distribution of the mean A theoretical probability distribution of the means of an infinite number of samples given size from a population. Standard error of the mean The standard deviation of the sampling distribution of the sample mean. Point estimation Involves calculating a single statistics to estimate the parameter. Confidence interval aka Interval Estimate Result adjusted by adding and subtracting the margin of error Null hypothesis The hypothesis that there is no significant difference between specified populations, any observed difference being due to sampling or experimental error. Alternate hypothesis Type I error false positive Type II error false negative Alpha level Indicates the area in the theoretical probability distribution that corresponds to the rejection of the null hypothesis. It s the area under the probability distribution that is outside (above and below) the confidence limits, i.e., it is the total "shaded" area. It is equal to 1.00 minus the size of the confidence interval, thus a 95% confidence interval has an alpha value of 5% (or, 0.05). Beta The probability of making a Type II error. Power The probability that a test will reject a false null hypothesis. Parametric tests Involve estimating a parameter Non-parametric tests Do not test hypotheses about population parameters. One-tailed test Is one in which the critical region is in only one end of the distribution. Two-tailed test Is one that uses both tails of a sampling distribution to determine the critical region for rejecting the null hypothesis. Critical region Acceptance region on the theoretical distribution, and to accept the null hypothesis otherwise. Degrees of freedom Is a concept used in statistical testing to indicate the number of components that are free to vary about a parameter. One-sample t-test Standard error of the difference SEd summarizes how much sampling error occurs, on average, when a mean difference score is computed, for samples of a given size. Independent groups t-test Is used when the participants in the two groups are not the same people, nor connected to one another in a systematic way. Paired t-test (Dependent group t test or correlated groups t test) Point biserial correlation Between-group varaiance Differences between groups Within-group variance Differences between people in the groups Sum of squares The sum of the square deviations around a mean. One-Way ANOVA There is a single independent variable whole effect on a dependent variable is under study. Multifactorial ANOVA When the effects of two or more independent variables on a dependent variable are studied simultaneously. Repeated measures ANOVA Can be used if the same people are exposed to three or more different conditions, or measured at three or more points at a time. Post hoc test Two Sampled t test Used when the independent variable is a nominal-level variable with two levels—that is, when two groups are being compared ( or one group, tested before and after) t-tests are used for testing the mean differences between two groups Two-sample t-tests are used when the dependent variable is an ______ or ________ level variable Interval or ratio. Cannot be nominal or ordi