IR WEEK 2

Question 1

define statistical power in relation to β error

Answer 1

the probability that a test will lead to rejection of the null hypothesis. (probability of attaining statistical significance)

Question 2

List the 4 functions that determine statistical power

Answer 2

significance criterion, variance, sample size, effect size

Question 3

Define Variance

Answer 3

as variance decreases, the power increases

Question 4

define sample size

Answer 4

the larger the sample the greater the statistical power

Question 5

define effect size

Answer 5

as effect size increases, then power increases

Question 6

Define the significance criterion

Answer 6

as error decreases, power increases. if you lower the alpha level, then you are requiring stronger evidence to determine significance, but means you increase your chances of missing a true effect.

Question 7

Define measurement error

Answer 7

the difference between the true value and the observed value

Question 8

define reliability

Answer 8

the extent to which a measurement is consistent.

Question 9

Define Validity

Answer 9

ensures that a test is measuring what it is intended to measure. implies that measurement is relatively free from error

Question 10

Define accuracy

Answer 10

agreement between measured result and actual/true value
(systematic errors affect accuracy)

Question 11

Define precision

Answer 11

repeatability or reproducibility of measurement
(consistent value does not imply correct value)

Question 12

Define Systematic error

Answer 12

consistent over or under estimation of the true value (predictable)

Question 13

define random error

Answer 13

due to chance, unpredictable (human error, simple mistake)

Question 14

define minimal clinically significant difference

Answer 14

the smallest difference in a measured variable that signifies an important rather than trivial difference in the patients condition

Question 15

This type of t-test
compares a sample mean to a given population mean
requires a normally distributed population and population mean is known
- the sample standard deviation won’t have a normal distribution (z distribution), because it is not a population in standard deviation

Answer 15

one sample t test

Question 16

compares two sample means
requires two normally distributed but independent populations, population mean is unknown

Answer 16

students/unpaired t test

Question 17

requires a set of paired observations from a normal population

Answer 17

paired t tests

Question 18

List the four assumptions when performing a t test

Answer 18

1. normal/gaussian distribution
2. randomly sampled
3. equal variances-
4. data measured

Question 19

what is a design that indicates one independent variable/factor with three or more variables?

Answer 19

one-way ANOVA determines if observed differences among a set of means are statistically significant from each other

Question 20

null hypothesis

Answer 20

proposes no statistical significance between a set of observations

Question 21

as error decreases

Answer 21

power increases

Question 22

If the probability of committing a type 1 error decreases

Answer 22

the probability of committing a type 2 error increases

Question 23

as variances decreases

Answer 23

then power increases

Question 24

the larger the sample size

Answer 24

the greater the statistical power

Question 25

as effect size increases

Answer 25

power increases

Question 26

as random error decreases

Answer 26

reliability increases

Question 27

first measurement is expected to move closer or regress, toward the group average (mean) on the second measurement

Answer 27

regression toward the mean

Question 28

what affects validity?

Answer 28

systematic error and extreme random error

Question 29

the degree to which the changes in the dependent variable are the result of manipulation of the independent variable

Answer 29

internal validity

Question 30

the degree to which the results of your sample can be inferred to the general population

Answer 30

external validity

Question 31

one sample compared to a population

Answer 31

one sample t test

Question 32

two sample groups

Answer 32

unpaired t test or paired

Question 33

this type of t test compares one set of measurements with a second set of measurements from the same sample.

Answer 33

PAIRED T-test, can be used to compare before and after.

Question 34

design indicates one independent variable/factor WITH 3 or more levels

Answer 34

one way ANOVA

Question 35

other independent variables/factors can be added to the mix. looking for interactions between independent variables.

Answer 35

two way anova

Question 36

Example: measure effects of 3 drugs and 3 diet regimens on blood pressure

Answer 36

can use two way ANOVA

Question 37

Example: measure effects of 3 drugs on blood pressure

Answer 37

one way ANOVA

Question 38

“within subjects” analogous to the paired t tests. measuring things at 2 different times

Answer 38

repeated measures ANOVA

Question 39

includes multiple dependent variables

Answer 39

MANOVA multivariate ANOVA

Question 40

effects of three different medications on diastolic and systolic blood pressure

Answer 40

MANOVA can be used bc there are two dependent variables.

Question 41

this post test looks at a comparison of each group to each group to determine if the specific null for that pair can be rejected

Answer 41

Tukey’s post test

Question 42

specifies type 1 error rate for each pairwise contrast, rather than for the “family”. is more powerful and more likely to detect significant difference

Answer 42

Newman-Keuls post test

Question 43

uses familywise error rate, therefore as # of comparisons increases, each comparison has to achieve a lower p value to achieve significance

Answer 43

Bonferroni analysis, A PRIORI TEST

Question 44

used to evaluate exploratory data, to evaluate the relationship between two measured variables

Answer 44

correlations

Question 45

a sample size of less than 15 and a correlation below r=0.45, does this demonstrate correlation

Answer 45

it would be considered a weak correlation

Question 46

using relationship between variables as a basis for prediction. draw conclusions about populations based on samples taken from that population

Answer 46

linear regression

Question 47

independent or predictor variable for linear regressions

Answer 47

variable X

Question 48

dependent or criterion variable

Answer 48

Variable Y

Question 49

uses statistical methods to find the “best fit” line (regression line)

Answer 49

linear regression

Question 50

points that do not seem to fit with the rest of the scores; lies outside the obvious cluster of scores

Answer 50

outliers

Question 51

uses linear regression to evaluate new procedures or equipment in clinical setting

Answer 51

comparison of methods

Question 52

perfect method agreement

Answer 52

y=x
m=1
b=0
r>0.99

Question 53

r^2 gives the percentage of total change in Y scores that can be explained by the X scores, will range between 0.00 and 1.00

Answer 53

coefficient of determination

Question 54

if you find a correlation of r=0.087 for the regression of blood pressure on age, then r2 = 0.76

Answer 54

76% of the change in blood pressure can be accounted for by knowing the age, the other 24% is due to an unknown or identified variable.

Question 55

linear regression can be used to predict Y when you know X if its within your data set. this is called

Answer 55

interpolation

Question 56

if you attempt to predict Y when you know X and you go beyond your data set, this is called

Answer 56

extrapolation