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## PSY302, Midterm #1 Study Guide

by: Emma Cochrane

81

0

1

# PSY302, Midterm #1 Study Guide PSY302

Marketplace > University of Oregon > Psychlogy > PSY302 > PSY302 Midterm 1 Study Guide
Emma Cochrane
UO
GPA 3.6

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This study guide is to help you prepare for the first midterm!
COURSE
Statistical Meth Psych
PROF.
Jordan Pennefather
TYPE
Study Guide
PAGES
1
WORDS
CONCEPTS
Statistics, Psychology, psych, psy302
KARMA
50 ?

## Popular in Psychlogy

This 1 page Study Guide was uploaded by Emma Cochrane on Sunday April 24, 2016. The Study Guide belongs to PSY302 at University of Oregon taught by Jordan Pennefather in Winter 2016. Since its upload, it has received 81 views. For similar materials see Statistical Meth Psych in Psychlogy at University of Oregon.

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Date Created: 04/24/16
NOTE: all formulas can be found on Canvas and will be given for the test, but names aren’t included! Population: any complete set of observations; the entire set of individuals, objects, or events a researcher wants to study. Vary in size, often quite large. ex: all UO undergrads ex: all students present for class Sample: a subset of observations from a population, used to infer what is true of the population ex: students in this class ex: 5 cards picked from a shuﬄed deck Variables and Data Variable: characteristic or condition that changes or has diﬀerent values for diﬀerent individuals Data (plural): measurements or observations of a variable Data set: a collection of measurements or observations A datum (singular): a single measurement or observation, commonly called a score or raw score Statistics and Parameters Samples have statistics statistic = numeric value that describes a sample Latin letters (M, s) Populations have parameters parameter = numeric value that describes a population Greek letters Variables discrete variables: (ex: # siblings, major, biological sex) separate, indivisible categories no values can exist between two categories continuous variables: (ex. time, weight, height) inﬁnite number of possible values between each point measured Measurement Scales (NOIR) Nominal (categories) Ordinal (in order) Interval (equal intervals) Ratio Mean, Median, and Mode from Frequency Table mean: (ΣX)/n median: (n+1)/2 mode: most frequently occurring number in a set of data Normal or Gaussian Curve relative frequency (no #s on y-axis) many naturally occurring distributions approximate this curve mean, median, and mode are the same Skewed Distribution tail goes out left → negative distributions (median higher than mean) tail goes out right → positive distributions (mean higher than median) Shade Implications for Central Tendency Negative skew: >50% will be above average Positive skew: >50% will be below average use median for seriously skewed data Variance and Inferential Statistics goal for inferential statistics is to detect meaningful and signiﬁcant patterns in research results variability in the data inﬂuences how easy it is to see patterns error variance: variability due to error Z-Scores identify and describe location of every score in the distribution standardize an entire deviation takes diﬀerent distributions and makes them equivalent and comparable one we know the mean and standard deviation of scores, we can turn any raw score into a standardized score (z-score) allows us to know the exact location of x in the distribution of scores sign tells us if it’s above or below the mean # tells us distance between score and mean need to know the value of X, M, and SD to understand exact location all 3 pieces combine to make 1 value population z-score: (X-M)/Ơ, sample z-score: (x-m)/s Properties of Z-score Distributions mean is always 0 SD is always 1 when an entire set of scores is transformed into z-scores, it’s called a standard deviation Area Under the Curve the total area under the normal curve is 1 z-scores will divide the distribution into the tail and body z-scores above the mean are positive, below the mean are negative, but proportions will always be positive because z-scores deﬁne the sections, the proportions of the area apply to any normal distribution distribution is symmetrical so proportion of + and - z-scores are the same The Unit Normal Table (this is found in Appendix B in your book!) the proportion for only a few z-scores can be shown graphically How to use: If Z is positive probability of score >Z is the tail probability of score <Z is the body If Z is negative probability of score >Z is body probability of score <Z is tail can only be used with normal distributions Assumptions of Z-Test random sampling was used sampling distribution is normal m and s are known and the value of s is unchanged by treatment Percentile Ranks % of people who scored less than you ex: 65th percentile means you scored better than 65% of people Central Limit Theorem for any population with a mean m and a standard deviation of ơ, the sampling distribution of the mean for a sample size n will: have a mean of m increasingly approach a normal distribution as n increases have a standard deviation of ơ/√n Hypothesis testing is used in statistics to make educated guesses about a population The Logic: using a sample to infer the truth about the population helps us decide whether means are diﬀerent enough to conclude signiﬁcance of the results Hypothesis testing helps us decide between two options: the diﬀerent between the sample and the population can be explained by a sampling error the diﬀerent between the sample and the population is too large to be explained by sampling error Hypotheses are always about population parameters Logic of Hypothesis Testing What you are doing: using a sample to infer the parameters of an unknown population Steps: ask the research question state hypothesis (null and alternative) about inferred population we test upside down (we try to prove ourselves wrong) use hypothesis to set decision rule collect data – ideally a random sample from a population analyze data to test hypothesis see if sample data is consistent with your hypothesis make a decision Two Choices Reject do this when statistic falls in the extreme tails of the null distribution of sample means Fail to reject do this when statistic falls in the middle of the distribution

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