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03Statistics Stenemo > 02 Basics of Statistics 2 > Flashcards

02 Basics of Statistics 2 Flashcards

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1
Q

How do you test hypotheses?

A

build statistical models of the phenomenon of interest

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2
Q

simplest statistical model

A

mean

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3
Q

How do statistical models allow you to gain confidence in the alternative hypothesis?

A
  • fits the data well

- explains a lot of variation in the scores

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4
Q

Most models are:

A

linear - based on a straight line

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5
Q

Types of fits for statistical models

A
  • good fit
  • moderate fit
  • poor fit
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6
Q

interferential statistics

A

determines whether the alternative hypothesis is likely to be true

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7
Q

p-values

A

probability that the result is a chance finding

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8
Q

common threshold for confidence

A

95% confident that the result is genuine and not due to chance

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9
Q

What p-value is statistically significant?

A

P less than 0.05

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10
Q

easiest way to assess statistical models

A

look at the difference between the data observed and the model fitted

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11
Q

measures of how well the model fits the actual data

A
  • deviance
  • sum of squared erros (SS)
  • variance
  • standard deviation
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12
Q

deviance

A

difference between the observed data and the model of the mean

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13
Q

deviance =

A

observed score = mean value (x-bar)

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14
Q

disadvantages to using deviance

A
  • some values are negative and some positive

- can cancel themselves out

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15
Q

sum of squared errors (SS)

A

square the difference between observed score and mean value

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16
Q

disadvantage of using SS

A

SS value is dependent on the amount of data collected

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17
Q

With more data point, SS value is

A

higher

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18
Q

variance (s2)

A

average error between the mean and observed scores

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19
Q

variance equation

A

SS/(n-1)

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20
Q

What does variance build upon?

A
  • SS value

- takes amount of collected data into account

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21
Q

standard deviation (s)

A

square root of variance

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22
Q

benefit to using standard deviation

A

ensures that measure of average error is in the same units as the original measure

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23
Q

standard deviation equation

A

√(SS/(n-1))

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24
Q

What does a small s indicate?

A
  • data points are close to the mean

- the model is a good fit

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25
What does a large s indicate?
the mean is not an accurate representation of the data
26
Standard deviation provides information about
how well the mean represents the sample data
27
If you take several samples from the same population, the samples will ____________, so it is important to understand _______________.
- differ slightly | - how well the sample represents the population
28
standard error related to standard deviation
standard error is similar measure to the population as standard deviation is to the sample
29
sampling variation
samples from the same population will vary slightly because they contain different members of the population
30
sampling distribution
frequency distribution of the sample means from the population
31
average of the sample means =
mean of the population
32
standard error of the mean
standard deviation of the sample means
33
What does standard error of the mean measure?
variability between the means of different samples of the population
34
standard error
√(standard error of the mean)
35
central limit theorem
as samples get large, the sampling distribution has a normal distribution with a mean equal to the population mean
36
central limit theorem applies to
more than 30 people in a sample
37
Because it's impossible to collect hundreds of samples, you must rely on
approximations of standard error
38
What do confidence intervals provide?
another approach to assess the accuracy of the sample mean as an estimate of the population mean
39
confidence interval - range of values
range (2) values within which the researchers think the population value falls
40
What do you need to calculate confidence intervals?
must know - s - x-bar
41
most common CIs
95% | 99%
42
95% CI means
95% likely that the population mean falls between the two values
43
99% CI means
99% likely that the population mean falls between the two values
44
What lies at the center of the CI?
mean
45
small CI
sample mean must be very close to the true mean
46
wide CI
sample mean is not similar to the true mean and thus is a bad representation of the population
47
How can systematic variation be explained?
by the statistical model (IV)
48
Can unsystematic variation be explained by the statistical model?
- no | - not attributable to IV
49
test statistic
- variance explained by the model | - variance not explained by the model
50
examples of test statistics
t-stat f-stat x2 stat
51
larger test statistic
more unlikely it occurred by chance
52
larger test statistic =
- lower p-value | - more likely the test statistic is statistically significant
53
A hypothesis can be ________ or _________
- directional | - non-directional
54
directional hypothesis
one-tailed test
55
non-directional hypothesis
two-tailed test
56
The prediction of direction must be made ______
prior to collecting data
57
The one/two tailed test has a statistical advantage
one-tailed test
58
Why does the one tailed test have a statistical advantage to a two-tailed test?
- researcher needs a smaller test statistic to find significant results - must have research to support the use of a one-tailed test
59
different effect sizes
- Cohen's D - Pearson's correlation coefficient - response measures (MCID most common)
60
Why do we need effect sizes?
A statistically significant finding does not mean that the finding is clinically useful or of a magnitude that is meaningful
61
When can effect sizes be calculated?
post-hoc to determine the magnitude of a statistically significant effect
62
power =
1-beta
63
How can power be useful?
- calculate sample size a priori | - calculate power of the study post-hoc
64
g power
free program that can be downloaded to determine sample size to achieve a desired level of power
65
higher test statistic = lower
p-value

03Statistics Stenemo (17 decks)

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