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Segars: Introduction to Biostatisitics Flashcards

1
Q

What is a Null hypothesis (H0)

A

-there will be NO change!!!!!

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2
Q

What is aternative hypthesis (H1)?

A

-there WILL be a change between groups

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3
Q

What does it mean when something is normally distributed?

A

-when mean, median, and mode are all equa

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4
Q

% found withing 1 SD of the mean on a normally distributed curve?

A

-68%

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5
Q

2 SD’s

A

-95%

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6
Q

3SD’s

A

-99.7%

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7
Q

What does a + skew curve look like?

A
  • tail pointing to right
  • Mean>median
  • mode will always be that peak
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8
Q

negative skew

A
  • tail pointing to left

- Mean< median

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9
Q

What is skewness?

A

-a measure of the asymmetry of a distribution

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10
Q

What are the 3 kinds of statistical test?

A
  • Nominal
  • ordinal
  • Interval
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11
Q

Nominal

A
  • no magntitude
  • no consistency of scale
  • named… only 2 things
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12
Q

Ordinal

A
  • yes magnitude
  • no consistency of scale
  • ex: pain scale
  • +3 categories
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13
Q

Interval/ratio

A
  • yes magnitude
  • yes consistency of scale
  • ex: age
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14
Q

What are the required assumptions of interval data?

A
  • normally distributed
  • equal variances :use levene’s test
  • Randomly-derived and independent
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15
Q

1 of the 4 key questions to selevting the correct statistical test?

A
  • What DATA LEVEL is being recorded

- is it Nominal, ordinal, or interval???

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16
Q

2

A
  • what type of comparison/assessment is desired?
  • Correlation
  • regression
  • survival comparison
  • group comparison
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17
Q

What are the values for correlation?

A

-+1 and -1, upwards slope and downwards slope (45 degrees)

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18
Q

If we are looking for a correlation test, what are the possibilities?

A
  • Nominal= contingency coefficient
  • Ordinal= Spearman correlation
  • Interval= pearson correlation
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19
Q

What do we have to know about pearson correlation?

A

-just assesses for linear correlation, there may still be non linear correlations present if pearson correlation non-signif.

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20
Q

After we decide if it is nominal, ordinal, or interval, what do we do next?

A
  • look for 5 things
  • 2 groups
  • > or = 3 groups
  • Proportion of events (survival)
  • Measure of correlation
  • Prediction or association
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21
Q

What are survival tests commonly represented by?

A

-a kaplan meier curve

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22
Q

What will all survival tests have on the bottom?

A

-time on x axis

23
Q

If it is survival, what are the possibiliities?

A
  • Nominal= log rank test
  • Ordinal= cox-proportional hazards test
  • interval= Kaplan Meier test
24
Q

what is a regresssion?

A
  • provides a measure of the relationship between variables by allowing the prediction about the dependent, or outcome, variable knowing the value/category of independent variables (IV’s)
  • also able to calculate OR for a measure of association and control for confounding IVs
25
If it is outcome prediction/association (or regression), what are the possibilites?
- Nominal= logisitic regression - ordinal= multinomial logistic regression - Interval= linear regression
26
What are the last 2 questions we have to ask before we pick which statistical test we want to do?
- How many groups | - is the data independent or related
27
Nominal 2 groupd of indy data
-pearson's chi square test
28
Nominal >=3 groups indy
-chi square test of independence
29
>= 2 groups with expected cell count of >5?
- fisher's exact test | - aka, not a lot of people
30
What are the key words for "paired" or "related" data?
- pre vs post - before and after - baseline vs end
31
Ordinal and 2 groups of indy data?
-Mann-whitney test
32
>= 3 groups of indy data
-Kruskal-Wallis test
33
What does the Mann whitney and kruskal wallis tests compare?
-the median values between the groups
34
2 groups of pair/related data
-wilcoxon signed rank test
35
>=3 groups of paired/related data
- Friedman test | - both compar median values again
36
post hoc tests for 3 or more gorup comparisons
- student newman keul test - dunnett test - dunn test: good for when groups are NOT of equal size, we need that for the other 2....
37
Interval 2 grops of indy data
-student t test
38
Interval >=3 groups of indy data
- Analysis of variance (ANOVA) - both tests compare the MEANS - Multiple analysis of variance (MANOVA)
39
Interval >= 3 groups of independent data w/ confounders?
- analysis of Co variance (ANCOVA) | - MANCOVA
40
Interval, 2 groups, paired/related
-Paired t-test
41
>=3 groups, interval, related
- repeated measures ANOVA | - Repeated measured MANOVA
42
Interval, >= 3 groups, paired, confounders
- repeated measures ANCOVA | - Repeated measures MANVOVA
43
Interval data post hoc tests for 3 or more group comparisons
- student-newman-keul test - Dunnett test - Tukey or Scheffe tests - Bonferroni corretion - Dunn test: rememebr, NOT EQUAL SIZE FOR THIS ONE, BUT YES EQUAL FOR THE OTHER 2
44
What are the 4 questions we have to ask when selecting the correct statistical test?
- What data level is being recorded? - What type of comparison is desired? - how many groups? - Indy or related?
45
What is a type 1 error?
- rejecting a null hypothesis when it is actually true and YOU SHOULD HAVE ACCEPTED IT YOU DUMB ASS! - insert picture guys that look the same but we said that they are different - same, yet we said they were different
46
What is a Type 2 error?
- accepting a null hypothesis when you should have rejected it - insert pic of a fat guy next to a skinny guy and we said that they were the same... different, yet we said it was the same
47
What is power (1-B error)?
-the ability of a study design to detect a true difference if one truly exists between group comparisons, and therefore the level of accuracy in correctly accepting/rejecting the null hypothesis
48
What is the relationship between sample size and power?
-the larger the sample size, the greater the likelihood of detecting a difference if one truly exists
49
What 3 things go into sample size determination?
- minimum difference between groups deemed relevant/important - expected variation of measurement - alpha and beta error rates and confidence interval
50
What is the p value
-probability of making a type 1 error
51
What is the magic number for p value?
- 5% or 0.05 | - if below that, then we can go ahead and reject the null hypothesis
52
What is the other thing that we use besides p value that is based on variation in sample and sample size?
- Confidence Interval (CI) | - 95% was blue
53
When are the stats NOT significant for the CI?
-if CI crosses 1.0 (for ratios) or 0.0

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