Statistics Primer Flashcards

1
Q

Histogram

A
  • How many people awnser the same
  • Creates a bell curve
  • The awnser that is the most is in the middle
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2
Q

Mode

A

Most common number

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3
Q

What three things have a central tendency?

A
  • Median
  • Mean
  • Mode
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4
Q

Histograms ____ things together. The larger the ____ the less information on the graph. The smaller the ____ the more precise the data.

A

bin (all 3)

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5
Q

____ is the best way to talk about distibution of responses

A

Standard Deviation

SD is related to the average

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6
Q

Flatter and wider distribution = ____ SD

A

larger

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7
Q

% distributions on a bell curve

A

The distribution of observations for a sample approximates a normal distribution
* 68% of observations within 1 SD of mean
* 95% of observations within 1.96 SD of mean
* 99% of observations wihtin 3 SD of mean

  • Mean ± 1 SD commonly reported (68% of data)
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8
Q

The other 5% of the ND is considered

A

rare

Split 2.5% on each tail

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9
Q

What is inferential statistics?

A

Estimated population characteristics using sample data

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10
Q

Inferential statistics looks for effects through ____ and ____

A
  • Hypothesis Testing (How different two groups are)
  • Relationships (How similar two groups are)
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11
Q

Inferential statistics - sample characteristics

A
  • Inclusion/exclusion criteria define the population
  • These criteria limit your generalization
  • The more cases you sample, the more confident you can be your sample represents the population well (greater precision)
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12
Q

The looser the sample criteria =

A

better general application

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13
Q

Sampling Error

A
  • Each sample results in a different estimate of the population mean
  • Larger samples = better estimates of the population mean (more accurate, therefore less error)
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14
Q

Standard error of the mean (SE)

A
  • Estimate of how close a particular sample mean is likely to be the population mean
  • This is different than SD
  • SE = sample SD/sqrt(sample size)
  • Larger samples allow us to be more accurate of the actual population mean (smaller SE)
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15
Q

Samples that are smaller end up =

A

flatter and wider distribution

More SD

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16
Q

Having a distribution table that have more than ____ allows for normal distribution tables. The probability is ____ that any random sample falls within this range.

A
  • 50 samples
  • 95%

Not always possible; the more powerful the measure the less samples needed.

17
Q

95% Confidence Interval

A

Range of values expect the true population mean to lie within estimated confidence interval.

18
Q

Explain effect of sample size on distribution with graph

A
  • Small sample size = wider distibutions
  • The more subjects decreases the range of scores you would expect by chance alone

Can determine sample size through g power

19
Q

For smaller samples, what statistic do we use?

A
  • t-test for 95% confidence interval
  • As n (number) increases, SE decreases = more narrow confidence interval
  • 50 - infinitity = normal distibution
20
Q

When do we want to be 99% confident over 95% confident?

A
  • High risk situations
  • Drug interactions
21
Q

The width of the confidence interval is affected by what three things?

A
  • Sample size
  • How confident you want to be (95% vs 99%)
  • Sample Variability (SD); measurement error
22
Q

Confidence intervals tell you..

A
  • What values you should and should not expect by chance
23
Q

Hypothesis testing looks at the chance that the data occurs…

A

by chance or is real data

24
Q

Alpha

A
  • Describe the possibility of concluding groups are different when they are not
  • Alpha influences Type 1 error
  • Used in hypothesis testing
25
increasing the alpha =
not accepting randomness as much
26
Alpha level
* Your tolerance for declaring a sample mean as significantly different if it falls outside the expected range by chance * Typical error tolerane is 5%
27
Explain whether these groups are the same or different
* If accepting a 5% chance these are different by chance (a = 0.05), then these groups are different. * The means have to overlap into the 95% of the other group to be similar.
28
By changing from a 5% alpha to a 1% (99% confidence) this results in...
* Decreased chance of false significant conclusions * Type 1 error: have an increased probability of NOT finding a difference
29
Beta
* Probability of incorrectly concluding that the groups are not different (Type II error) * Typical value acceptable for clinical research is 20%
30
How to determine the power of test?
1 - Beta (1 is true value; B is the probability of incorrectly concluding the groups are not different)
31
Power of a test must be ____ to be acceptable for clinical research
80%
32
What are the 4 factors that can affect power?
* Alpha level * Difference between group means * Within group variability * Sample Size
33
How does alpha affect the power level?
* Raising the alpha level increase power * Also increases Type 1 Error * One tailed tests also increase power BUT you must justify why you are not looking at the other tail | One tailed
34
How does the difference between groups affect power?
* Larger group differences increase power (Better chance they are different)
35
How does within group variability effect power?
* More precise outcome measures: SD is decrease, decreased score variability = increases power * Less precise outcomes measure: SD increases, increased score variability = less power
36
How does a sample size affect power?
* More subjects = more power * More likely to be closer to true population
37
When comparing two distibutions, what does it mean when they overlap a lot?
They are similar! Less likely to see significant difference.