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29 Cards in this Set
- Front
- Back
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We use T-test when... |
1. We don't know Pop SD 2. N is less than 30 |
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S can be used to estimate what? |
Population SD |
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The T distribution |
-Looks more like normal distribution as the sample size increases -Symmetrical -Flatter in middle -Thicker on ends -T depends on V |
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Degrees of freedom (V) |
How much the data is free to vary |
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When to use a one tailed test |
When there is a directional question "Are women better men in...?" |
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When to use a two-tailed test |
Non-directional question "is there a difference between men and women?" |
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Significance levels can be expressed as.. |
The probability (alpha) the results are due to chance "P <.05, there is less than 5% chance that the differences are due to sampling error" |
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DF for single T-test |
N-1 |
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DF for Independent T-test |
N1 + N2 - 2 |
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DF for Dependent T-test |
Number of pairs -1 |
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DF for Pearson r |
N - 2 |
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Assumptions underlying the T-test |
1. Scores must be interval or ratio 2. Scores must come from random sample 3. Population must be normally distributed 4. Populations must have approx. equal variance |
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Variance is .. |
Sometimes more important than central tendency |
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Chi Square test X2 |
A sum of squared normal deviates |
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Properties of X2 Chi Square |
-Expected value is V -Skew is (2/V)V2 -Variance is 2V -Mode is V-2 -X2 is additive -As V Increases to infinity, it becomes normal
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F is defined as... |
Ratio of two chi squares divided by their respective degrees of freedom
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When to use Chi Square |
When you're comparing a single variance to a population variance |
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When to use the F test |
When you are comparing two independent variances |
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Correlation |
Tells us how strongly RELATED two variables are X CONTINIOUS & Y CONTIN. |
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Regression |
Allows us to use scores on one variable to PREDICT an outcome on the second variable |
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The Scatterplot visually portrays... |
Relationship between variable Y Vertical X Horizontal |
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Scatterplot can show |
The direction of a relationship |
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Correlation, Pearson r properties |
-1 < Z < 1 0 Means nothing, no relationship Bigger the number, the stronger the relationship |
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r Squared tells us |
how much of the variance in one variable is accounted for by the variance of another variable |
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Ex. Variance R= .60 so R2= .36 this tells us |
36% of the variance in x is associated with change in y and 64% not accounted for by y |
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Pearson r only tests for.. |
Linear relationships, its designed for two continuous variables |
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Statistical Decision theory |
When using stats to predict the state of affairs in the world, what are the possibly outcomes and how likely are those outcomes |
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Type I error: "jumping the gun" Making a change when you shouldn't
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You reject the null hypothesis when the null hypothesis is actually true |
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Type II error: "missing the boat" something is going on but we missed it |
Fail to reject the null, when there is actually something going on |
