BS - Epi/Biostat (Statistics)

Question 1

What are the 3 measures of central tendency? Define each.

Answer 1

(1) Mean = (sum of values) / (total number of values); (2) Median = middle value of list of data sorted from least to greatest; (3) Mode = most common value

Question 2

How is the median of an even number of values calculated?

Answer 2

If there is an even number of values, the median will be the average of the middle two values

Question 3

What are 2 measures of dispersion? Define each.

Answer 3

(1) Standard deviation = how much variability exists from the mean in a set of values (2) Standard error of the mean = an estimation of how much variability exists between sample mean and true population mean

Question 4

What is the equation for standard error of the mean? How does SEM change as sample size increases?

Answer 4

SEM = sigma / (n)^1/2; sigma = SD, n = sample size; SEM decreases as n (sample size) increases

Question 5

What else is normal distribution called?

Answer 5

Gaussian, also called bell-shaped

Question 6

How do the mean, median, and mode compare in normal distribution?

Answer 6

Mena = Median = Mode

Question 7

In a normal distribution, what percentage of values lie within: (1) 1 SD of mean (2) 2 SD of mean (3) 3 SD of mean? Draw a visual depicting this.

Answer 7

(1) 68% (2) 95% (3) 99.7%; See p. 56 in First Aid 2014 for visual near middle right of page

Question 8

What are 3 types of nonnormal distributions?

Answer 8

(1) Bimodal (2) Positive skew (3) Negative skew

Question 9

What defines bimodal distribution? Give an example. Draw a sketch of it.

Answer 9

Suggests two different populations (e.g., metabolic polymorphism such as fast vs. slow acetylators, suicide rate by age)

Question 10

How does the mean, median, and mode typically compare in a positive skew distribution? Describe this distribution’s shape. Draw a sketch of it.

Answer 10

Typically, mean > median > mode; Asymmetry with longer tail on right; See p. 56 in First Aid 2014 for visual

Question 11

How does the mean, median, and mode typically compare in a negative skew distribution? Describe this distribution’s shape. Draw a sketch of it.

Answer 11

Typically, mean < median < mode; Asymmetry with longer tail on left; See p. 56 in First Aid 2014 for visual

Question 12

State the statistical hypotheses, defining each.

Answer 12

(1) Null (H0): Hypothesis of no difference (e.g., there is no association between the disease and the risk factor in the population) (2) Alternative (H1): Hypothesis of some difference (e.g., there is some association between the disease and the risk factor in the population)

Question 13

Draw a chart depicting the definitions of outcomes of statistical hypothesis testing.

Answer 13

See p. 56 in First Aid 2014 for visual at bottom right of page

Question 14

What are 2 options for a correct result in the outcome of statistical hypothesis testing?

Answer 14

(1) Stating that there is an effect or difference when one exists (null hypothesis rejected in favor of alternative hypothesis) (2) Stating that there is not an effect or difference when none exists (null hypothesis not rejected).

Question 15

What are 2 types of incorrect result in the outcome of statistical hypothesis testing?

Answer 15

(1) Type I error (alpha) (2) Type II error (beta)

Question 16

What symbol denotes Type I error? What defines it? What is another name for Type I error?

Answer 16

Type I error (alpha); Stating that there is an effect or difference when none exists (null hypothesis incorrectly rejected in favor of alternative hypothesis); Also known as false-positive error

Question 17

What is alpha in the context of statistical hypothesis testing? Explain how it is used.

Answer 17

Alpha is the probability of making a type I error. p is judged against a present alpha level of significance (usually < .05). If p < 0.05, then there is less than a 5% chance that the data will show something that is not really there.; Think: “Alpha = you sAw a difference that did not exist (e.g., convicting an innocent man)”

Question 18

What symbol denotes Type II error? What defines it? What is another name for Type II error?

Answer 18

Type II error (beta); Stating that there is not an effect or difference when one exists (null hypothesis is not rejected when it is in fact false); Also known as false-negative error

Question 19

What is beta in the context of statistical hypothesis testing? Explain how it is related to power.

Answer 19

Beta is the probability of making a type II error. Beta is related to statistical power (1 - Beta), which is the probability of rejecting the null hypothesis when it is false. ; “Beta = you were Blind to a difference that did exist (e.g., setting a guilty man free)”

Question 20

What are 3 factors that increase power? What effect do these factors have on Beta?

Answer 20

Increase power and decrease beta by: (1) Increase sample size (2) Increase expected effect size (3) Increase precision of measurement; Think: “if you increase sample size, you increase power. there is POWER IN NUMBERS”

Question 21

What defines meta-analysis? Describe its power.

Answer 21

Pools data and integrates results from several similar studies to reach an overall conclusion; Increase statistical power

Question 22

What is the limitation of meta-analysis?

Answer 22

Limited by quality of individual studies or bias in study selection

Question 23

What defines confidence interval?

Answer 23

Range of values in which a specified probability of the true population measure would be expected to fall

Question 24

What is the range/calculation of confidence interval?

Answer 24

CI = range from [mean - Z(SEM)] to [mean + Z(SEM)]

Question 25

What CI is often used, and to what does it corresponding?

Answer 25

The 95% CI (corresponding to p = .05) is often used

Question 26

What is the z-score for 95% and 99% CI?

Answer 26

For the 95% CI, Z = 1.96. For the 99% CI, Z = 2.58.

Question 27

What happens if the 95% CI for a mean difference between 2 variables includes 0?

Answer 27

If the 95% CI for a mean difference between 2 variables includes 0, then there is no significant difference and H0 is not rejected

Question 28

What happens if the 95% CI for odds ratio or relative risk includes 1?

Answer 28

If the 95% CI for odds ratio or relative risk includes 1, H0 is not rejected

Question 29

What happens if the CIs between 2 groups do not overlap?

Answer 29

If the CIs between 2 groups do not overlap => significant difference exists

Question 30

What happens if the CIs between 2 groups overlap?

Answer 30

If the CIs between 2 groups overlap => usually no significant difference exists

Question 31

What is the purpose of t-test? Give an example.

Answer 31

Checks differences between means of 2 groups; Think: “Tea is meant for 2”; Example: comparing the mean blood pressure between men and women

Question 32

What is the purpose of ANOVA? Give an example.

Answer 32

Checks differences of 3 or more groups; Think: “3 words: ANalysis Of VAriance”; Example: comparing the mean blood pressure between members of 3 different ethnic group

Question 33

What is the purpose of chi-square (x^2)? Give an example.

Answer 33

Check difference between 2 or more percentages or proportions of categorical outcomes (not mean values); Think: “pronounce Chi-tegorical”; Example: comparing the percentage of members of 3 different ethnic groups who have essential hypertension.”

Question 34

What values does the pearson correlation coefficient (r) always lie between?

Answer 34

r is always between -1 and +1

Question 35

How does the correlation between 2 variables change as the the absolute value of r move closer to 1?

Answer 35

The closer the absolute value of r is to 1, the stronger the linear correlation between the 2 variables

Question 36

What does a positive versus negative r mean?

Answer 36

Positive r value => positive correlation. Negative r value => negative correlation.

Question 37

What value is usually reported regarding r?

Answer 37

Coefficient of determination = r^2 (value that is usually reported)