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Evaluative Sciences II > Biostatistics for Dentistry > Flashcards

Biostatistics for Dentistry Flashcards

1
Q

True or False: Statistics are important because they allow us to understand information and make clinical decisions based on data.

A

True

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2
Q

What are two types of data?

A
  1. Quantitative

2. Categorical

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3
Q

Quantitative data can be divided into ____ and ____.

A

Continuous

Discrete

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4
Q

Continuous data has values that are ____.

A

all possible, no set range

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5
Q

Discrete data has values that are ______.

A

only possible within a range

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6
Q

Categorical data can be subdivided into _____ and _____.

A

Nominal

Ordinal

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7
Q

What is Nominal data ?

A

data falls into a category but has no order (race/ethnicity)

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8
Q

What is ordinal data?

A

data has a specific order within a category

never, sometimes, always

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9
Q

What are four ways to describe quantitative data?

A

Mean
Median
Mode
Standard Deviation

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10
Q

The ____ is sensitive to extreme values.

A

Mean

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11
Q

The ____ is less sensitive to extreme values.

A

Median

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12
Q

____ is the measure of how much the individual data varies around the mean.

A

Standard Deviation

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13
Q

What are three ways to describe categorical data?

A
  1. frequency
  2. Percentage
  3. Correlation
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14
Q

Correlation shows whether there is a _____ between an independent variable (x) and dependent variable (y).

A

linear relationship

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15
Q

A correlation coefficient (r) can lie between ___ and ___.

A

-1 and +1

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16
Q

A positive correlation coefficient value indicates ____.

A

as independent (x) increases,, dependent (y) increases

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17
Q

A negative correlation coefficient value indicates ____

A

as independent (x) increases, dependent (y) decreases

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18
Q

The closer (r) is to +1 or -1, the ______ the relationship.

A

stronger

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19
Q

The square of correlation (r^2) is the fraction of ______ in Y explained by X.

A

variation

ex. if r =0.9 then r^2 = 0.81

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20
Q

The higher the r^2 value, the _____ the fit of the regression line.

A

better

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21
Q

A _____ is an explanation for certain observations

A

hypothesis

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22
Q

The null hypothesis usually states that ___________.

A

there is no difference between two groups being compared or no effect of a product or intervention

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23
Q

What is the alternative hypothesis?

A

the one the researcher believes to be the truth; usually states that there is a difference between two groups being compared or an effect of a product

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24
Q

Results of testing a hypothesis can be ______ (1>2) or ______ (1=2)

A

directional

non-directional

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25
Q

Is the following statement null or alternative?

=the population mean for men is the same as the population mean for women.

A

Null (there is no difference)

26
Q

What is a Type I error?

A

rejecting the null hypothesis when it is actually true

trying to act like there is a difference when there is NOT one

27
Q

What is the level of statistical significance for Type I error?

A

alpha

28
Q

Alpha is commonly set to ______ and is interpreted as the maximum chance (____%) of incorrectly rejecting the null hypothesis when it is actually true.

A

0.05

5%

29
Q

What is a Type II error?

A

failing to reject the null hypothesis when it is actually false in the population

Saying there is no difference when there actually is a difference

30
Q

The probability of a type II error is described as _____.

A

beta

31
Q

How is “power” calculated?

A

1 - beta

32
Q

True or False: Power is related to the sample size.

A

True

33
Q

What is the “p value”?

A

the probability, assuming the null hypothesis is true, of seeing an effect (as extreme or more extreme than that in the study) by chance

34
Q

_____ the null hypothesis if the p-value is LESS THAN OR EQUAL TO alpha.

A

Reject

35
Q

Fail to reject the null hypothesis if the p-value is _____ than alpha.

A

greater

36
Q

What are confidence intervals?

A

a range of values about a sample statistic that we are confident about that the true population parameter lies

37
Q

What is the most common confidence interval?

A

95%

38
Q

Confidence interval = 95%
If the data collection and analysis is repeated over and over, the confidence interval will _____ the correct value 95% of the time.

A

include

39
Q

What are three ways to test statistics?

A

t-test
Chi Square
Anova

40
Q

What is a t-test and when is it used?

A

a test used to determine whether the mean of a continuous outcome variable differs significantly between two independent groups
-used for a continuous outcome

41
Q

True or False: When using a t-test, the alternative hypothesis may be directional or non-directional.

A

True

42
Q

The ____ ____ t-test can be used when the outcome variable of interest is only being examined in one group.

A

One-sample

43
Q

The ____ _____ t-test can be used when subjects are in pairs and their outcomes are compared within each pair (including where observations are taken on the same subjects before and after a given intervention).

A

Matched-pair

44
Q

True or False: A t-test can measure up to 3 groups.

A

False, two groups only

45
Q

Ex. Null: women = men; alternate: women don’t = men. alpha= 0.05, p-value-0.006.
What can be concluded?

A

p < alpha

reject the null hypothesis: conclude that there is a difference between men and women

46
Q

What is a chi-squared test?

A

a test used during examination of CATEGORICAL data to compare the proportion of subjects in each of TWO groups who have a dichotomous outcome

ex: periodontitis in diabetics vs non-diabetics
null: no association, alternate: association present; if p-value is less than alpha, reject the null

47
Q

What is an ANOVA used for?

A

Analysis of Variance:

a statistical method that allows for comparison of several population means (more than two!)

48
Q

What is the null hypothesis when comparing 3+ groups? When using ANOVA, when can you reject the null hypothesis?

A

Null: means of all groups are equal

Reject when the p-value of F-Statistic is less than or equal to alpha

49
Q

True or False: ANOVA is the only analysis that uses p-value.

A

False, it is the only one to use F-statistic

50
Q

True or False: A p-value tells about clinical relevance and study quality.

A

False!

51
Q

P-values have _____ significance, but not _____ significance.

A

statistical

not clinical

52
Q

Statistical inference only tells about the ______ in making inference from your study population to the source population.

A

role of chance or random error

53
Q

True or False: Statistics do not tell about causality.

A

True

54
Q

What is bias?

A

systemic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure’s effect on disease

55
Q

Two important types of bias: _____ and ____.

A

selection bias- choosing patients

information bias- gathering info

56
Q

A situation in which a non-causal association between a given exposure and an outcome as a result of the influence of a third variable.

A

Confounding

57
Q

Confounding can be either a _______ or ____.

A

confounding variable

confounder

58
Q

A variable is confouding if it is a known ______ of the outcome or if it is associated with ______.

A
  1. known risk factor of the outcome

2. associated with the exposure but not the result of

59
Q

How do you evaluate confouding?

A

assess the measure of association within strata

60
Q

True or False: Confounding can lead us to conclude a causal relationship when there is none.

A

True

Evaluative Sciences II (5 decks)

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