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Stats Chap 2
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- 1⍺-levelThe probability of making a Type I error (usually this value is .05).
- 2Alternative HypothesisThe prediction that there will be an effect (i.e., that you experimental manipulation will have some effect or that certain variables will relate to each other).
- 3β-levelThe probability of making a Type II error (a maximum value of .2).
- 4Bonferroni CorrectionA correction applied to the ⍺-level to control the overall type I error rate when multiple significance test are carried out. Each test conducted should use a criterion of significance of the ⍺-level (normally .05) divided by the number of tests conducted. This is a simple but effective correction, but tends to be too strict when lots of tests are performed.
- 5Central Limit TheoryThis theorem 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. We also know from this theorem that the standard deviation of the sampling distribution (i.e., the standard error of the sample mean) will be equal to the standard deviation of the sample(s) divided by the square root of the sample size (N).
- 6Cohen's dAn effect size that expressed the difference between two means in standard deviation units.
- 7Confidence IntervalFor a given statistic calculated for a sample of observations (e.g., the mean), the confidence interval is a range of values around the static that are believed to contain, with a certain probability (e.g., 95%), the true values of the statistic (i,e., the population value).
- 8Degrees of FreedomEssentially it is the number of "entities" that are free to vary when estimating some kind of statistical parameter. In a more practical sense, it has a bearing on significance test for many commonly used test statistics (such as the F-ratio, t-test, chi-square statistics) and determines the exact form of the probability distribution for these test statistics.
- 9DevianceThe difference between the observed values of a variable and the value of that variable predicted by a statistical model.
- 10Effect SizeAn objective and (usually) standardized measure of the magnitude of an observed effect. Measures include Cohen's d, Glass's g and Pearson's correlations coefficient, r.
- 11Experimental HypothesisSynonym for alternative hypothesis.
- 12Experimentwise Error RateThe probability of making a type I error in an experiment involving one or more statistical comparisons when the null hypothesis is true in each case.
- 13Familywise Error RateThe probability of making a Type I error in any family of tests when the null hypothesis is true in each case. The "family of tests" can be loosely defined as a set of tests conducted on the same data set and addressing the same empirical question.
- 14FitIt's the degree to which a statistical model is an accurate representation of some observed data.
- 15Linear ModelA model that is based upon a straight line.
- 16Meta-AnalysisThis is a statistical procedure for assimilating research findings. It is based on the simple 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.
- 17Method of Least SquaresA method of estimating parameters (such as the mean, or a regression coefficient) that is based on minimizing the sum of squared errors. The parameter estimate will be the value, out of all of those possible, that has the smallest sum of squared errors.
- 18Null HypothesisThe reverse of the experimental hypothesis, it says that your prediction is wrong and the predicted effect doesn't exist.
- 19One-Tailed TestA test of a directional hypothesis. For example, the hypothesis "the longer I write this glossary, the more I want to place my editor's genitals in a starved crocodile's mouth" requires a one-tailed test because I've stated the direction of the relationship.
- 20ParameterWhen you fit a statistical model to your data, that model will consists of variables and parameters: variables are measured constructs that vary across entities in the sample, whereas parameters describe the relations between those variables in the population.
- 21PopulationIn statistical terms this usually refers to the collection of units to which we want to generalize a set of findings or a statistical model.
- 22PowerThe ability of a test to detect an effect of a particular size (a value of .8 is a good level to aim for).
- 23SampleA smaller (but hopefully representative) collection of units from a population used to determine truths about that population (e.g., how a given population behaves in certain conditions).
- 24Sampling DistributionThe probability distribution of a statistic. We can think of this as follows: if we take a sample from a population and calculate some statistic (e.g. the mean), the value of this statistic will depend somewhat on the sample we took. As such the statistic will vary slightly from sample to sample. If, hypothetically, we took lots and lots of samples from the population and calculated the statistic of interest we could create a frequency distribution of the values we got. The resulting distribution is what the sampling distribution represents: the distribution of possible values of a given statistic that we could expect to get from a given population.
- 25Sampling VariationThe extent to which a statistic (the mean, median, t, F, etc.) varies in samples taken from the same population.
- 26Standard ErrorThe standard deviation of the sampling distribution of a statistic. For a given statistic (e.g., the mean) it tells us how much variability there is in this statistic across samples from the same population. Large values, therefore, indicate that a statistic from a given sample may not be an accurate reflection of the population from which the sample came.
- 27Standard Error of the Mean (SE)The standard error associated with the mean.
- 28Test StatisticA statistic for which we know how frequently different values occur. The observed value of such a statistic is typically used to test hypotheses.
- 29Two-Tailed TestA test of a non-directional hypothesis. For example, the hypothesis "writing this glossary has some effect on what I want to do with my editor's genitals" requires a two-tailed test because it doesn't suggest the direction of the relationship.
- 30Type I ErrorOccurs when we believe that there is a genuine effect in our population, when in fact there isn't.
- 31Type II ErrorOccurs when we believe that there is no effect in the population, when in fact there is.
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- Psychology Karteikarten am Stats Chap 2, erstellt von Sarah Church am 07/10/2016.
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