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44 Cards in this Set
- Front
- Back
- 3rd side (hint)
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If a confound is present and the DV varies in different conditions |
The experimenter is Not able to determine whether the changes were caused by the IV (s) or by the confound |
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Confounds influnce |
The measures of central tendency for different conditions |
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Independent-sample design |
Random assignment, keep conditions other than IV the same, Distribute potential confounders across conditions |
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Dependent sample design |
Counterbalancing Balances order or practice effects |
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Advantages of more conditions |
Efficiently compare multiple conditions, Less chance of type1Error ( compared to two group exp ..) |
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Comparing T-test and anova |
Independent samples t -randomly selected samples, dv normally distributed, dv measured using ratio or interval scale, homogeneity of variance, independent groups... Independent Anova- randomly selected. Dv normally distributed, dv measured using ratio or interval scale, homogeneity of variance, independent groups |
Both have the same comparison |
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Comparing stat hypotheses independent t-test and anova (population differences) |
T test- = or not = Anova- not all (u's) are equal or at least population mean is different from another |
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Advantage of one way anova test over multiple t test |
Reduce type 1 error |
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Type 1 error |
When we reject the null hypothesis when its true Can only occur if results of inferential test show significant difference Occurs when we say a manipulation had an effect when it did not The experimenter directly control the type 1error risk by selecting a alpha level |
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The lower the alpha level the .... |
Lower the risk of type 1 error |
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The more test you do the. ... |
The greater the risk of type 1 error |
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Anova reduces the probability of a type ...... ____ error |
1 ( type 1 error) |
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Experiment-wise error rate |
Is the type 1error rate for entire set if inferential tests Probability that at least one of the t-test will have type 1error Anova allows making many comparisons while keeping the experiment-wise error @.05 |
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Bonferroni adjustment |
Setting lower alpha level for many tests to reduce type 1 error Not deal- increases type 2 error ( reducing power) |
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Independent samples are also called |
Randomized anova |
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Independent samples are also called |
Randomized anova |
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What is anova |
Inferential stats to determine if there are differences between more than 2 groups
Resulting in F statistic
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What do we calculate in anovav |
F statistic (obt) |
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What is f statistic |
Ratio compares variance between sample means to the within |
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Mean square (MS) in anovabis used for |
Variance |
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Formula for Fobt |
Ms between /ms within |
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Formula for Fobt |
Ms between /ms within |
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Between sample |
Reflecting that a treatment was used |
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Within sample |
Treated similarly or the groups are similar |
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Both f and t statistic are |
Ratios if obtained difference (numerator) to the expected difference by chance if null hypothesis was true (denominator) |
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T= |
Actual obtained difference between 2 sample means / estimated standard error ( difference expected with no treatment effect) |
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How do we partition variance the total variability |
Variance between treatments And Variance within treatments |
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Between groups variability |
Variability caused by independent variable |
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Within groups variability /(error variability) |
Variability due to factors such as individual differences Errors in measurement In differences nit due to independent v |
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The lower the error variability the greater the... |
F statistic |
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The lower the error variability the greater the... |
F statistic |
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F ratio if no treatment effect ( f ratio would be close to 1) |
F= 0+ differences due to chance/ differences due to chance |
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F distribution is ..._____ skewed |
Positively |
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F distribution is ..._____ skewed |
Positively |
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Sig f ratio indicates?! |
Unlikely to occurred by chance |
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Use what test to determine which means differ |
Post hoc ( follow up tests) |
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Use what test to determine which means differ |
Post hoc ( follow up tests) |
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Tukey is what kind of test |
Pos hoc |
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What is tukey used for |
Pairwise comparisons. . formula calls for dividing the differences between pairs by means of standard error |
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Type 1error |
Rejecting null when its true |
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Type 2 error |
Fail to reject the null when its false |
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Type 1 error (false positive error) |
Null hypothesis is true , but was rejected ( proven untrue ) after testing |
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Type 2 error ( false negative) |
Null hypothesis is false but (after testing) was proven true |
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Partial counter balancing |
Randomized counter balancing Latin square |
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