Lecture 8

Question 1

descriptive statistics

Answer 1

are methods that help researches organize, summarize, and simplify the results obtained from researches studies

Question 2

inferential statistics

Answer 2

are methods that use the results obtained from samples to help make generalizations about populations

Question 3

what are statistics

Answer 3

summarize the data from as sample, typically used to estimate the same parameter from a population

Question 4

how do we use statistics

Answer 4

1. descriptive statistics: summarize data from sample
2. inferential statistics: extend outside of sample
   - determine if group/conditions differences are from chance

Question 5

parameter

Answer 5

is a summary value that describes a population. A common example of a parameter is the average score fro a population

Question 6

frequency distributions

Answer 6

demonstrates number of instances a variable takes each possible value

Question 7

frequency distributions for interval/ratio scale data

Answer 7

• histogram

- polygon

Question 8

frequency distributions for nominal/ordinal data

Answer 8

-bar graph

Question 9

central tendency

Answer 9

is a statistical measure that identifies a single score that defines the center of a distribution. The goal of central tendency is to identify the value that is most typical or most representative of the entire group

Question 10

3 measures of central tendency

Answer 10

• mean
• median
• mode

Question 11

mean

Answer 11

measure of central tendency obtained by adding the individual scores, then dividing the sum by numbers of scores. The mean is the arithmetic

Question 12

median

Answer 12

measures central tendency by identifying the score that divides the distribution in half. Scores listed in order this would be the center number.

Question 13

mode

Answer 13

measures central tendency by identifying the most frequently occurring score in the distribution

Question 14

standard deviation

Answer 14

is the square root of the variance and provides a measure of variability by describing the average distance from the mean

Question 15

variance

Answer 15

measures the variability of the scores by computing the average squared distance from the mean. (how large is the spread of data

Question 16

2 measures of variability

Answer 16

1. variance
2. standard deviation
   these are measurements of error

Question 17

graph rules

Answer 17

• independent/treatment on x-axis

- dependent/outcome variable on y-axis

Question 18

SD in normal distributions

Answer 18

• 99.7% of the data is within 3 SD of the mean
• 95% of the data is within 2 SD of the mean
• 68% of data is within 1 SD of the mean

Question 19

sampling error

Answer 19

• a sample could be unrepresentative of the population

- inferential statistics help us determine the probability that thats the case

Question 20

probability

Answer 20

• the likelihood of an event happening by chance

- number of outcomes/the total number of outcomes

Question 21

hypothesis tests: null vs alternative hypothesis

Answer 21

null hypothesis: Ho: theres nothing there
alternative hypothesis: H1: aligns with predictions
Ho: P=0
H1: P>0; P<0

Question 22

sampling distribution

Answer 22

all possible b=values of a statistic for an infinite number of samples of a given size

Question 23

sample distribution

Answer 23

all scores from the one sample

Question 24

null hypothesis

Answer 24

is a statement about the populations being examined and always says that there is no effect, no change or no relationship
-any patterns are nothing more than chance

Question 25

what things do we need for hypothesis tests

Answer 25

1. the null hypothesis 2. the sample statistic 3. the standard error 4. the test statistic 5. the alpha level (level of significance)

Question 26

types of test statistics

Answer 26

1. Interval/ratio data (at least 1 measurement) - T-test: single score, 2 scores, 2 groups - ANOVAS (f-test): >2scores, >2 groups 2. nominal/ordinal data (all measurements) - chi-sqaure tests: proportions in categories

Question 27

types of tests

Answer 27

1. correlational analysis | 2. regression analysis

Question 28

correlational analysis

Answer 28

- calculate the correlation | - test the correlation

Question 29

regression analysis

Answer 29

- calculations the regression equation | - test the regression equation

Question 30

the alpha level

Answer 30

for a hypothesis test us the max probability that research result was obtained simply by chance ex) test with alpha level is .01 means the test demands are less than 1% that results are caused only by chance

Question 31

types of errors in hypothesis testing

Answer 31

1. type 1 errors 2. type 2 errors false positive false negative

Question 32

degrees of freedom

Answer 32

defined as the number of scores in the sample that are free to vary for a given statistics

Question 33

type 1 error

Answer 33

sample data appear to show a significant effect but, in fact is not effect in the population

Question 34

type 2 error

Answer 34

sample data do not show a significant effect when, in fact there is a real effect in the population

Question 35

false positive

Answer 35

innocent man in jail

Question 36

false negative

Answer 36

guilty man on the street

Question 37

2 things that make it more likely yo find statistically significant results

Answer 37

- sample size: the larger the more likely 2. variability - more overlap in the graph when there is more variability, you want less variability in data

Question 38

replicability crisis

Answer 38

some of the scientific findings that we've accepted as "true" are not as stable as we might thinking -when other researchers try to replicate the study, they did not find the same effect

Question 39

effect sizes

Answer 39

1. cohen's d 2. R2 3. confidence intervals

Question 40

cohen's d

Answer 40

-differences between means expressed in SD units

Question 41

r2

Answer 41

square the correlation coefficient - 0.81 = good correlation - 0.24 = not good correlation

Question 42

confidence intervals

Answer 42

estimate of range that will include population parameter