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Exam 2 Study Guide

by: Shannan Dillen

32

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3

Exam 2 Study Guide PSY 2110

Shannan Dillen
Ohio
GPA 3.9

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This study guide covers all you need to know for the second exam. Probability, different hypothesis, the steps to determining hypotheses, and t-testing.
COURSE
Statistics for Behavioral Sciences
PROF.
S. Tice-Alicke
TYPE
Study Guide
PAGES
3
WORDS
CONCEPTS
Stats, Statistics, Exam 2
KARMA
50 ?

Popular in Psychology (PSYC)

This 3 page Study Guide was uploaded by Shannan Dillen on Saturday October 1, 2016. The Study Guide belongs to PSY 2110 at Ohio University taught by S. Tice-Alicke in Fall 2016. Since its upload, it has received 32 views. For similar materials see Statistics for Behavioral Sciences in Psychology (PSYC) at Ohio University.

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Date Created: 10/01/16
Exam 2 Study Guide Probability  Analytical View: an analysis of possible outcomes to define probability o Ex: A bag contains 1 black marble and 9 white marbles. What is the probability of drawing a black marble? P(A) = A / (A + B) if event could be A or B and all outcomes are equally likely o Reduce to decimal form – 4 decimal places  Relative Frequency View: defines probability in terms of past performance/outcomes o Ex: Waiting for a bus and after many days of doing this you notice that 75/100 times the bus is late. P(bus late) = .75  Subjective View: not based on actual numbers or calculations; defined in terms of personal belief in an outcome’s likelihood o What you believe will happen o May not be accurate, but important because it influences behavior o Ex: weather, likelihood of rejection if we ask someone out, likelihood of being punished if committing a crime  Independent Events – occurrence of one event has no effect on the probability of the occurrence of another event o Ex: dice rolls  Mutually Exclusive Events – the occurrence of one event makes the occurrence of another event impossible o If it’s one, it can’t be the other o Ex: gender, days of the week  Conditional Probability – probability that one event will occur, given that another event has occurred o One has to happen for the other to happen Hypothesis Testing  5 Steps:  State the hypothesis o Hypothesis – statement about the population parameter of sample data  Null hypothesis – always states there is no difference; things are the same o HO  Alternate Hypothesis (Research Hypothesis) – opposite of the null; there is a difference o H1  Set up criteria for making decision about the null o Draw graph – is it one-tailed or two-tailed?  One-tailed: one region of rejection, p stays on one side  Directional – specific direction prediction made  Two-tailed: two regions of rejection, split p in half  Non-directional – no specific direction prediction made  CV – critical value – mark off the region of rejection o Find Z-scores from table based on regions of rejection  Analyze the data o Sampling Distribution of the Means  σ”xbar”Standard Error of the Mean (same thing conceptually as standard deviation)  equals σ / √n  Z = (???? - µ) / (σ/√n)  Make decision about the null o Compare the computed value that you got from step 3, to the critical values form step 2  If in region of rejection, then reject the null  If not in region of rejection, then retain the null/fail to reject the null  Write conclusion o Ex: null was rejected  There is a significant difference in the mean IQ with the IQ pill compared to the IQ without the IQ pill. o Ex: null was retained  There is no significant difference in the mean IQ with the IQ pill compared to the IQ without the IQ pill. o Identify independent and dependent variables o Significant – statistical test has been performed and using probability, there is a difference  Nothing to do with size of difference  Sampling Distribution of Means – the distribution of all possible random sample means when an infinite number of random samples of the same size are randomly selected  Central Limit Theorem – defines 3 properties of Sampling Distribution of Means (SDM) o Regardless of the shape of the raw score distribution, the SDM is nearly a normal distribution. o The mean of the SDM is always equal to the mean of the raw score population. o There is a mathematical relationship between variables of the raw scores and variability of sample means. Independent Samples t-test  Use t-test when sigma is unknown  df – degrees of freedom – the number of components in a calculation that are free to vary o df = n – 1  Test is used to determine if there is a significant difference between the population means of 2 random independent samples o All about differences between 2 groups  Assumptions: o Populations from which the samples are drawn are normally distributed. o Homogeneity of Variance: σ1= σ 22  Two population variances are equal  If one sample variance is no more than 4 times the other sample variance and the sample sizes are roughly equal, then good to go; not violation the assumption o The observations in each sample are independent. 2 2  If n’s are equal: √(S 1n +1S /n2) 2  If n’s are unequal: √(S /p + 1 /n p 2 o S p pooled variance = ((n – 1)1S ) + 1n – 1)(2 )) / (2 + n )1– 2 2  Confidence Intervals – range of values that we are confident contains the population parameter ̅ o CI .95 or .99 ± (tCV(S ”x bar”  Always use the t critical value from a two-tailed test o Conclusion Example: We are 95% confident the population mean short term memory for students at Memoryville College falls in the interval 8.98 to 11.02.

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