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| require certain assumptions (normal distribution, interval or ratio scores) about the raw score population being represented |
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| procedures that do not require assumptions about population represented by sample-- nominal or ordinal data |
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mathematical procedures for organizing, summarizing, and describing the important characteristics of a sample data
Give us information about our sample data
Help us to generalize from the sample to population |
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| mathematical procedures for deciding whether a sample relationship represents a relationship that actually exists in the population |
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probability of rejecting a false null hypothesis
1-beta |
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Increase n
Reduce error variability
Strong manioulation
Decrease beta |
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| Want a more powerful test because |
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| shows if null hypothesis is false |
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| occurs when we reject a true null hypo |
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| occurs when we fail to reject a false hypo |
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| Indicates how consistently differences in the dependent scores are caused by changes in the independent variable |
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| The larger the effect size |
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| the more consistently the scores in each condition are located at or close to the mean for that condition, so the more consistent is the influence of the indep var |
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| effect size for t-test, the amount of variance obtained |
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| effect size, amount of variance obatined |
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| the differences between the scores in each condition and the mean of the condition |
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| Caused by unreliability and fluctuating extraneous variables |
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| controlling threats to reliability and validity and minimizing individual differences |
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| systematically changing the order of trials for different participants in a balanced way to counter the biasing influence of any one order. |
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| used to control individual differences: if there is a difference in someone make sure that diff is in both groups |
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| probs with counterbalancing |
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build in issue that you do not notgice
introduce confounds
upset something that was balanced
difficult to find people to fit |
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participants are assigned to a particular condition because they have already experienced or currently exhibit that condition of the variable
Do not truly manipulate the independent variable |
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| Quasi-independent variable |
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| We lay out the design and compare the scores between conditions as in a true experiment, but we only appear to administer the independent variable |
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More control of researcher, environmental, and task variables
Researcher only looks at a few relationships of the x scores obtained |
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| for each condition in one condition there is a comparable participant in the other conditions |
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| pros of w/in subjects design |
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| decreases error variability (individual differences), ratio gets bigger easier to see if treatment has effect, more powerful |
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| cons of w/in subjects design |
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| Danger to order of effects, Controlling effects |
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| testing indep variable with only independent samples |
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| Nonsystematic carryover effects |
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| ccur when the carry-over effects from one order of conditions do not balance out those of another order |
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| what kind of design are nonsystematic carry-over effects a problem |
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| Between-subjects designs are preferred if |
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| carry-over effects are nonsymmetrical, if the task does not allow repeated testing, or if extensive counterbalancing is unwise |
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| each participant in one condition “matches” a participant in the other condition(s) on one or more extraneous variable |
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| Experimenter wise error rate |
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| the probability of making a type 1 error when comparing all means in an experiment |
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| the sum of the squared deviations of a set of scores around a statistic |
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