Chapter 9: T-statistics Flashcards

1
Q

why do we use the t-statistic?

A

The t statistic doesn’t require any knowledge of the population standard deviation

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2
Q

what is the purpose of the t-statistic?

A

use sample data to test hypotheses about an unknown population mean

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3
Q

estimated standard error

A

Sm= s/√n

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4
Q

t-statistic formula

A

t= M-μ/ Sm

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5
Q

t-distribution

A

Family of distributions, one for each value of degrees of freedom

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6
Q

t-distribution vs. z-distribution

A

Flatter than the normal distribution
More spread out than the normal distribution
More variability (fatter tails) in the t distribution

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7
Q

columns of the z-distribution table

A

organized by a levels with common decisions rules listed

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8
Q

rows of the z-distribution table

A

for different sample sizes and the associated degrees of freedom

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9
Q

z-score vs t-score

A

The t statistic is like an estimated z score

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10
Q

hypothesis testing procedure for t-scores

A

Same four-step procedure as for z-scores

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11
Q

assumptions of the t-test

A

The values of the sample must consist of independent observations
The population that is sampled must be normal

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12
Q

estimated cohen’s d formula

A

Estimated d= M-μ/s

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13
Q

what is a small effect

A

r²= 0.01

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14
Q

what is a medium effect

A

r²= 0.09

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15
Q

what is a large effect

A

r²= 0.25

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16
Q

variance explained formula

A

r²= t²/t² + df

17
Q

3 types of effect sizes

A

Raw scores, Cohen’s d, variance explained

18
Q

raw scores

A

the mean difference we observe in the units of the variable we are working with

19
Q

cohen’s d

A

the mean difference converted into standard deviation units

20
Q

variance explained

A

the variance explained in the outcome by taking into account the independent variable

21
Q

confidence interval

A

a range of values that will contain the true population parameter

22
Q

how are confidence intervals specified

A

probabilities

23
Q

confidence interval formula

A

μ= M +- t(Sm)

24
Q

when is the t-statistic similar to the z-statistic

A

when there is a large sample size