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## Exam 3 study guide

by: Jacqueline Vilca

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# Exam 3 study guide PSY 292 Forster- Introduction to Biobehavioral Statistics for Non-Majors

Jacqueline Vilca
UM
GPA 3.9
PSY 292 Forster- Introduction to Biobehavioral Statistics for Non-Majors
Forster

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COURSE
PSY 292 Forster- Introduction to Biobehavioral Statistics for Non-Majors
PROF.
Forster
TYPE
Study Guide
PAGES
6
WORDS
KARMA
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This 6 page Study Guide was uploaded by Jacqueline Vilca on Saturday March 21, 2015. The Study Guide belongs to PSY 292 Forster- Introduction to Biobehavioral Statistics for Non-Majors at University of Miami taught by Forster in Spring2015. Since its upload, it has received 58 views.

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Date Created: 03/21/15
Study Soup Chapter 912 study guide CHAPTER 10 Hypothesis testing Descriptive Statistics The logic of null hypothesis testing is based on proofby counter example If you nd a case where the hypothesis is false then you can quotreject the hypothesisquot Null hypothesis Testing Formalizing Null Hypothesis testing There is the Null Hypothesis and the Alternative hypothesis In terms of the null hypothesis we can quotrejectquot or quotfail to rejectquot the null hypothesis We can NEVER accept the null hypothesis We can only fail to reject it We can NEVER reject the alternative hypothesis either NULL HYPOTHESIS TESTING DIRECTIONALITY The alternative hypothesis is stated It s the results you obtained that were obtained by chance The null hypothesis is the negation of the alternative hypothesis The alternative hypothesis is a statement of your results not being obtained by chance RULES If the alternative hypothesis is nondirectional the null hypothesis states that the variable has no effect If the alternative is directional then the null hypothesis states that the variable does not have any effect or has an effect in the opposite direction TESTING THE NULL HYPOTHESIS 1 Calculate the probability that we obtain scores from different parts of any sampling distribution 2 Making the assumption that the null hypothesis is true we can calculate the probability of obtaining the set of observed scores in our sample 3 If the probability of obtaining our results is suf ciently small we can reject the null hypothesis 4 If the probability is not suf ciently small then we fail to reject the null hypothesis Remember we cannot prove it only fail to reject it THE CUTOFF The cutoff value of probability that determines whether results are signi cant or not signi cant 005 or 5 DECISION RULES Beta represents the probability of wrongly rejecting the alternative hypothesis Alpha represents the probability of wrongly rejecting the null hypothesis The level of signi cance is considered the probability of wrongly rejecting the null hypothesis Based on the traditional cutoff mentioned above the level of signi cance of alpha is 5 TYPE I ERROR Wrongly rejecting the null hypothesis when the null hypothesis is actually true TYPE ERROR Wrongly rejecting the alternative hypothesis when the alternative hypothesis is true In research we are always searching to disprove our hypothesis We want to minimize our ability to falsely reject something that goes against our hypothesis null hypothesis CLARIFICATION OF ALPHA Alpha is the rate at which we would expect to reject a true null hypothesis It determines our ability to detect the truth DIRECITONAL AND NONDIRECTIONAL Directional is only evaluating only the tail of the distribution that is in the direction speci ed by the alternative hypothesis When the alternative hypothesis is nondirectional then we alpha level must be split between both sides of the distribution CHAPTER 11 The alpha level equals the probability of making a Type 1 error Beta equals the probability of making a Type II error Beta 1power To minimize making a type II error you want to maximize the power and minimize Beta Power The measure of the sensitivity of an experiment to detect a real effect of the independent variable The likelihood of concluding that there is a real effect when there really is an effect happening Real effect An effect that produces a change in the dependent variable Power of an experiment is the probability that the results of an experiment will allow rejection of the null hypothesis if it is false Overall we want to maximize the power Power values range from O to 1 The higher the power the easier it is to detect a real effect of the independent variable Usually there is a power of 080 or higher for the ideal but this is rare in practice 0406 is more common but it is not acceptable Power analysis is based on 2 components 1 Sample size and 2 Effect size you expect to detect Power WILL be different for different effect sizes When considering alpha remember that if alpha is made smaller the possible outcomes for rejecting the null hypothesis are decreased Power varies with 1 With N 2 With the size of the real effect of the independent variable 3 With the alpha level using a larger alpha level would increase the power Drawbacks to high alpha levels this may lead to a higher chance of making both Type I and Type II error Minimizing alpha and beta maximizes the likelihood that our conclusions will be correct If H0 is true then p 1 alpha If H0 is false then p 1Beta CHAPTER 12 Statistic The estimation of a parameter based on a sample that is used for a statistical test Examples of parameters include mean median variance and correlation Sampling distribution gives all values that the statistic can take and the probability of getting each value under the assumption that it had resulted just from chance alone Sampling distributions can be derived in 2 ways 1 Basic probabilities 2 Empirical sampling Sampling distribution of the mean The sampling distribution of the mean collects all the mean values from other samples and collects each value if sampling is random form the nullhypothesis population Sampling Distribution steps 1 Take each sample possibility of a xed size N 2 Calculate the mean of each sample you collected from 3 Calculate the probability of getting each mean value by chance alone Characteristics of the Sampling Distribution of the Mean 1 Is a distribution of scores where each score is a sample mean so it is a collection of means 2 Has a mean equal to the mean of the raw score population 3 Has a standard deviation equal to the standard deviation of the raw score population divided by the square root of N sample size 4 Is normally shaped but it does depend on the shape of the raw score population and on the sample size N Central Limit theorem This theorem explains how gathering the means of multiple sample sizes end up in a normal distribution shape The more means you collect the closer the distribution of all those means approaches the shape of a normal distribution Z Test Compares a sample mean to a population mean to determine whether the two means are signi cantly different from each other Critical Region The critical region for the rejection of H0 is the area under the curve that contains all the values of the statistic that allows rejection of the null hypothesis When to use a Z test 1 When the experiment involves a single sample mean 2 When the parameters of the nullhypothesis population are known 3 When the sampling distribution of the mean is normally distributed

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