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Flashcards in BS - Epi/Biostat (Statistics) Deck (37)
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1
Q

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

A

(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

2
Q

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

A

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

3
Q

What are 2 measures of dispersion? Define each.

A

(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

4
Q

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

A

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

5
Q

What else is normal distribution called?

A

Gaussian, also called bell-shaped

6
Q

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

A

Mena = Median = Mode

7
Q

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.

A

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

8
Q

What are 3 types of nonnormal distributions?

A

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

9
Q

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

A

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

10
Q

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

A

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

11
Q

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

A

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

12
Q

State the statistical hypotheses, defining each.

A

(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)

13
Q

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

A

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

14
Q

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

A

(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).

15
Q

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

A

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

16
Q

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

A

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

17
Q

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

A

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)”

18
Q

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

A

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

19
Q

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

A

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)”

20
Q

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

A

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”

21
Q

What defines meta-analysis? Describe its power.

A

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

22
Q

What is the limitation of meta-analysis?

A

Limited by quality of individual studies or bias in study selection

23
Q

What defines confidence interval?

A

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

24
Q

What is the range/calculation of confidence interval?

A

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

25
Q

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

A

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

26
Q

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

A

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

27
Q

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

A

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

28
Q

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

A

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

29
Q

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

A

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

30
Q

What happens if the CIs between 2 groups overlap?

A

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

31
Q

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

A

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

32
Q

What is the purpose of ANOVA? Give an example.

A

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

33
Q

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

A

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.”

34
Q

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

A

r is always between -1 and +1

35
Q

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

A

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

36
Q

What does a positive versus negative r mean?

A

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

37
Q

What value is usually reported regarding r?

A

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