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Unformatted text preview: Chapter 4 Research Hypothesis- H 1 ; predicts a difference between populations; can be directional or non-directional ; 1 2 Null Hypothesis- H ; predicts no difference between populations; 1 = 2 Population Distribution- the amount of people in a certain area Type 1 Error- reject the null hypothesis, but the null hypothesis is true We say 1 2 , when actually 1 = 2 The chance of making a Type 1 error is the same as the level of significance of the test- .05 significance level 5% chance of making an error Alpha Error ()- p<.05, <.05 Type 2 Error- we accept the null hypothesis, when the null hypothesis should be rejected We say 1 = 2 , when actually 1 2 Beta Error ()]- The more lenient the significance level, the smaller the chance for making a beta error Relationship Between Type 1 and Type 2 Error- If you protect against Type 1 error, chance of Type 2 error increases If you protect against Type 2 error, chance of Type 1 error increases Protecting against one, increases the chances of the other occuring Directional Hypothesis- when a clear direction is stated (increase, higher, lower); 1 tailed test; 1 > 2 or 1 < 2 Non- Directional Hypothesis- mixed theory, no direction prediction; 2 tailed; 1 2 Rationale for Hypothesis Testing- we want there to be differences between populations Conventional Levels of Significance- 5% (p<.05) & 1% (p<.01) Chapter 5 Population Distribution vs. Distribution of Means * Individual Scores * Means of groups of individual scores * Used when comparing...
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- Fall '07