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  1. alpha level
    the probability of making a Type I error (usually this value is .05)
  2. beta level
    the probability of making a Type II error (Cohen suggests a maximum value of 0.2)
  3. Central limit theorem (CLT)
    a theorem that states that when samples are large (above about 30) the sampling distribution will take the shape of a normal distribution regardless of the shape of the population from which the sample was drawn. For small samples the t-distribution better approximates the shape of the sampling distribution. The standard deviation of the sampling distribution will be equal to the standard deviation of the sample divided by the square root of the sample size.
  4. confidence interval
    for a given statistic calculated for a sample of observations, the CI is a range of values around that statistic that are believed to contain, with a certain probability ( 95%), the true value of that statistic.
  5. degrees of freedom
    number of entities that are free to vary when estimating some kind of statistical parameter.
  6. deviance
    the difference between the observed value of a variable and the value of that variable predicted by a statistical model
  7. effect size
    an objective and standardized measure of the magnitude of an observed effect. Measures include Cohen's d, Glass' g, and Pearson's r (r squared)
  8. fit
    the degree to which a statistical model is an accurate representation of some observed data.
  9. linear model
    a model that is based on a straight line
  10. meta-analysis
    a statistical procedure for assimilating research findings. Based on the idea that we can take effect sizes from individual studies that research the same question, quantify the observed effect in a standard way (using effect sizes) and then combine these effects to get a more accurate idea of the true effect in the population.
  11. one-tailed test
    a test of a directional hypothesis
  12. population
    refers to the collection of units to which we want to generalize a set of findings or a statistical model
  13. power
    the ability of a test to detect an effect of a particular size if an effect actually exists (aim for 0.8)
  14. sample
    a smaller collection of units from a population used to determine truths about the population from which it is sampled
  15. sampling distribution
    the probability of a distribution of a statistic. The distribution of possible values of a given statistic that we could expect to get from a given population.
  16. sampling variation
    the extent to which a statistic varies in samples taken from the same population
  17. standard deviation
    an estimate of the average variability (dispersion or spread) of a set of data measured in the same units of measurement as the original data. Square root of the variance.
  18. standard error
    the standard deviation of the sampling distribution of a statistic.
  19. standard error of the mean (SE)
    the full name of the standard error
  20. sum of squared deviation scores (SS)
    an estimate of total variability of a set of data. It is calculated first by calculating the deviance for each score and then the value is squared. Then it is summed for the SS.
  21. test statistic
    a statistic for which we know how frequently different values occur. The observed value of such a statistic is typically used to test hypotheses.
  22. two-tailed test
    a test of a non-directional hypothesis
  23. Type I error
    occurs when we believe that there is a genuine effect in our population, when in fact there isn't.
  24. Type II error
    occurs when we believe that there is no effect in the population when, in reality, there is.
  25. Variance
    an estimate of average variability of a set of a data. It is the SS divided by the number of values (n-1 for sample variances).
Card Set:
2011-11-03 05:36:17
AASP Ch2 Stats

Ch. 2: More Introductory Statistical Terms
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