Lectures 7-9 Flashcards

1
Q

what determines the probability that a sample of ratio-interval scale data was taken from a population with a pre-determined mean

A

One-sample t-test

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2
Q

what is an a priori constant

A

pre-determined mean (c)

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3
Q

(one-sample t-test, 2-tailed) H0

A

μ = c (calculated mean = pre-determined mean)

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4
Q

(one-sample t-test, 2-tailed) HA

A

μ ≠ c (calculated mean ≠ pre-determined mean)

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5
Q

(one-sample t-test) test statistic

A

t

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6
Q

(one-sample t-test) degrees of freedom

A

n - 1 (n = sample size)

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7
Q

what are 2-tailed tests

A

no direction of the difference (not less than or greater than) || can also denote if there is change or no change

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8
Q

what are 1-tailed tests

A

testing in a particular direction (more/less, better/worse, higher/lower)

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9
Q

What is HA in ratio-interval scale data

A

whatever the question asks, H0 will be opposite to the question asked

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10
Q

What happens when we have a -t in 2-tailed?

A

ignore (-) due to symmetrical t-distribution

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11
Q

What happens when we have a -t in 1-tailed?

A

check to see if HA is satisfied by inputting mean of the sample in order to ignore (-) || if HA is not satisfied, immediately accept H0

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12
Q

confidence intervals (CI) and limits

A

it can determined with 95% confidence that the mean of a population lies between two values (the lower and upper limit)

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13
Q

(confidence intervals) what does a smaller interval indicate?

A

a smaller standard error

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14
Q

what would happen if a 99% confidence interval was employed

A

the critical value = 0.01 = more confident the larger/wider the interval gets

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15
Q

what does the t-statistic calculate?

A

calculates the probability where the sample came from the population where H0 is true || t(0.05)(1 or 2 tailed)(DF=n-1)

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16
Q

What to consider when dealing with 2 samples of ratio-interval scale data

A

see if 2 samples have equal variances (variance-ratio test), compare the means of the samples, and the relationship between those samples (does S1 values increase while S2 values decrease or increases?)

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17
Q

What to consider when dealing with central tendency tests

A

are samples paired or independent? are the assumptions met?

18
Q

(central tendency tests) independent samples

A

values in one sample are not in any way related to values in the other sample

19
Q

(central tendency tests) paired samples

A

each value in one sample is associated with a particular value in the other sample (i.e.: via biological link)

20
Q

why use paired samples?

A

more direct comparison, reduces the extra extraneous variation (controlled) || cancels out other variables that may affect data

21
Q

(central tendency tests) two assumptions

A

RSNDP and homoscedasticity

22
Q

RSNDP

A

Randomly Sampled from a Normally Distributed Population || tests if data is normally distributed

23
Q

what test can test for normality

A

D’Agostino Pearson k^2

24
Q

what terms describe for equal or unequal variances

A

Homoscedastic/Heteroscedastic

25
what tests for homoscedasticity
variance-ratio test
26
(variance-ratio test) H0
σ1(^2) = σ2 (^2)
27
(variance-ratio test) HA
σ1(^2) ≠ σ2 (^2)
28
(variance-ratio test) test statistic
F(0.05)(2-tailed)(DF of larger s^2)(DF of smaller s^2)
29
What if the assumptions are not met?
must do data transformations
30
types of data transformations
logarithmic, square root, arcsine sq.rt.
31
which data transformation is the "go to" transformation when not dealing with specific scenarios
logarithmic: x' = log(x)
32
which data transformation is used when the original raw data are now counted as whole numbers
square root
33
which data transformation is used when you have proportions or percentages
arcsine square root transformations: x' = arcsin(sqrt(x))
34
what tests for differences in central tendency, BUT DO NOT test for differences in the PARAMETER
non-parametric tests
35
when are non-parametric tests appropriate to use for?
can use when assumptions are not met or for ranked data (ordinal-scale data), but run the risk of making a type II error
36
two examples of nonparametric tests
Mann-Whitney U test and Wilcox-Paired Sample test
37
Can one-sample t-tests, contingency tables, and goodness-of-fit tests be considered nonparametric tests?
one-sample t-test is a robust statistical test so it is NOT affected if assumptions are not met, and although contingency tables and goodness-of-fit tests are nonparametric, no assumptions are made
38
What does a two-sample t-test tests for
difference in the means of two independent samples of ratio-interval scale data
39
(two-sample t-test) 2-tailed H0/HA
H0: μ1 = μ2 || HA: μ1 ≠ μ2
40
(two-sample t-test) 1-tailed H0/HA
H0: μ1 (>,≥,,≥,
41
(two-sample t-test) test statistic
tests if the population means are equal
42
(two-sample t-test) degrees of freedom
DF= DF1 +DF2