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Year 2 - Semester 1 > KNPE 251 2/2 > Flashcards

KNPE 251 2/2 Flashcards

1
Q

Alternative Hypothesis

A

a statement, or position that is the positive viewpoint of your research question

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2
Q

directionality

A

whether there is direction in the hypothesis

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3
Q

null hypothesis

A

a statement or position, that is the skeptical viewpoint of your research question

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4
Q

Alpha (type I error)

A

probability of rejecting null when it is true

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5
Q

p-value

A

is the probability of seeing your data or something more extreme under the null hypothesis

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6
Q

statistical significance

A

is the conclusion that a set of data are unlikely to come from the null hypothesis

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7
Q

false negative

A

tests are negative when it should be positive

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8
Q

false positive

A

test results are positive when they should be negative

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9
Q

Type II error

A

probability of failingto rejective the null hypothesis when it is false

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10
Q

Single-sample T test

A

a statistical test that compares a sample mean of a numerical variable to a reference value. The null distribution is a t- distribution

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11
Q

Paired measurements

A

are two measurements taken from the same sampling unit

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12
Q

paired-sample t test

A

is a statistical test that compares the difference between paired measurements of a numerical variable to a reference value. The null distribution is a t- distribution

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13
Q

two-sample t-test

A

is a statistical test that evaluates whether the mean of a numerical variable for one group is different from the mean of another group. the null distribution in a t-distribution

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14
Q

expected contingency table

A

table of expected frequencies under the null hypothesis

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15
Q

independence In contingency tables

A

refers to the cells in the table having equal relative proportions across the levels of each variable independently

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16
Q

interaction in contingency tables

A

refers to the cells in the table not having equal relative proportions across levels of each variable

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17
Q

chi-squared distribution

A

the distribution of chi-squared scores expected from repeatedly sampling a statistical population where the null hypothesis is true. It is the null distribution for hypothesis testing with categorical data

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17
Q

chi-squared test

A

is a hypothesis test used with categorical data

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18
Q

chi-squared score

A

is the measure of the distance between two contingency tables. If the contingency tables are an observed and expected table, then measures the distance between sample data and the null hypothesis

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19
Q

X^2

A

the measure of difference between two contingency tables

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20
Q

association

A

is a pattern whereby one variable increases (or decreases) with a change in another variable. There is no implied causation between the variables

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21
Q

Bivariate normal distribution

A

is a normal distribution for two numerical variables that can be used to describe a statistical population where there is an association between the variables. Often used to describe the set up for correlation tests

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22
Q

correlation coefficient

A

the statistical test used to evaluate a sample correlation coefficient against a null hypothesis

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23
Q

correlation test

A

is a measure of association between two numerical variables.

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24
Q

p=-1 (correlation coefficient)

A

perfect negative association

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25
Q

p=0 (correlation coefficient)

A

no association

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26
Q

p=1 (correlation coefficient)

A

perfect positive association

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27
Q

Pearsons correlation coefficient

A

is the statistical test used to evaluate a sample correlation coefficient against a null hypothesis

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28
Q

r

A

is the statistical test used to evaluate a sample correlation coefficient against a null hypothesis

29
Q

dependant variable

A

response variable

30
Q

independent variable

A

predictor variable

31
Q

intercept

A

value of the response variable when the predictor variable is zero

32
Q

Linear regression

A

statistical test used to evaluate whether changes in one numerical variable can predict changes in another numerical variable

33
Q

link function

A

one of three parts of linear regression; connects the systematic component to the random component

34
Q

predictor variable

A

numerical variable used to predict the response variable

35
Q

random component

A

one three parts to linear regression; describes the probability distribution for sampling error

36
Q

residual

A

the difference between the observed data point and the predicted value

37
Q

residual variance

A

average squared residual value across all data points

38
Q

response variable

A

numerical variable predicted by the predictor variable

39
Q

slope

A

the parameter in a linear regression that describes the amount that the response variable increases (or decreases) for every unit change in the predictor variable

40
Q

statistical model

A

a mathematical model that incorporates both the relationship among variables and how the data are generated

41
Q

sum of squares

A

another name for the residual variance of linear regression

42
Q

systematic component

A

one of three parts of a linear regression; describes the mathematical relationship that connects the predictor variable and the response variable

43
Q

heteroscedasticity

A

term used to describe residual patterns that are not homoscedastic

44
Q

homoscedasticity

A

an assumption of a linear regression stating that the residuals have equal variance across the predictor variable

45
Q

independence (assumption)

A

an assumption of linear regression stating that the residuals sequentially independent from each other

46
Q

Linearity (assumption)

A

an assumption of linear regression stating that the response variable is a linear function of the predictor variable

47
Q

Normality (assumption)

A

an assumption of linear regression stating that the residuals are not normally distributed

48
Q

Shapiro-Wilks test

A

statistical test to quantitatively evaluate the assumption that the residuals are normally distributed

49
Q

F

A

used in the F-test to quantify the ratio of two variances

50
Q

F-score

A

used in the F-test to quantify the ratio of two variances

51
Q

F-test

A

statistical hypothesis test used to evaluate whether the variances of two groups are different

52
Q

analysis of variance (ANOVA)

A

common name given to statistical tests based off the F-distribution

53
Q

group variation

A

the variation between the group means and the overall grand mean

54
Q

residual variation

A

the variation between the sampling units and the group means

55
Q

contrast statement

A

A test of the difference in means between two groups in an ANOVA

56
Q

Family of contrasts

A

the set of all contrast statements used for a set of data

57
Q

family-wise error rate

A

the type I error rate for the family of contrasts

58
Q

post hoc tests

A

secondary tests uses to evaluate what groups have different means in an ANOVA

59
Q

Turkey HSD test

A

A type of post hoc test that evaluates all possible contrast statements

60
Q

Additivity

A

when the response to the combination of two levels is simply the sum of the two

61
Q

cell

A

the group of sampling units that corresponds to the joint level of two categorical variables

62
Q

interaction

A

when the response to the combination of two levels is not the simple sum of the two

63
Q

interaction plot

A

a specialized plot that highlights the interaction pattern between two categorical variables

64
Q

Main effects

A

another name for a categorical variable in a two-factor ANOVA

65
Q

two-factor analysis of variance

A

two-factor ANOVA statistical test used to evaluate the change in a numerical across two categorical variables

66
Q

Sources of Variation

A

are the partitioning of the data set by factor means, interactions and residuals that form the basis of F-tests

67
Q

If the p value is greater than or equal to alpha we…

A

fail to reject null hypothesis

68
Q

if p value is less than alpha we…

A

reject null hypothesis

69
Q

tradeoff between error rates type I and II

A

when one increases the other decreases, they are inversely related

70
Q
A

Year 2 - Semester 1 (6 decks)

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