STATS

Question 1

When the data are normally distributed and conform to a bell-shaped curve, what are percents that ligh between the mean +/- 1, 2, and 3 SD respectively

Answer 1

When the data are normally distributed and conform to a bell-shaped curve, 67%, 95%, and 99.7% of the observations lie between the mean +/- 1, 2, and 3 SD respectively (choice C).

Question 2

The median is

Answer 2

also
known as the 50th percentile. It is the value that lies in the center of the data points when they are arranged in numerical order:
10, 11, 11, 11, 13, 13, 14, 14, 15, 15, 16, 17, 18, 22, 24 Bottom Line: The median is also known as the 50th percentile.

Question 3

Qualitative variables are divided into

Answer 3

nominal and ordinal variables.

Question 4

A nominal variable is

Answer 4

named category. An example is an individual’s favorite ice cream flavor (chocolate, vanilla, or rocky road).

In the clinical setting, one often sees this broken down into a yes or no answer, as in survival of the patient or the presence or absence of a complication (bionary is nominal)

When a nominal variable consists of only two categories (such as yes and no), it is also referred to as a dichotomous variable.

Question 5

An ordinal variable

Answer 5

usually is seen in ranking scales,

such as in injury severity scores.

An example is how miserable an individual feels on a particular day, measured on a scale of 1 to 10;

Question 6

Ordinal variables are tested with

Answer 6

nonparametric statistics.

The Wilcoxon signed rank test is the most common nonparametric test used primarily to examine the differences between two paired treatments using ordinal variables.

Question 7

Chi-Squared

Answer 7

This test determines differences between treatments for nominal variables (ice cream flavor stats).

Question 8

Unpaired t-test

Answer 8

This test is used to compare two unpaired treatments using quantitative variables.

number stats

Question 9

The Wilcoxon signed ranked test

Answer 9

This test is used to compare two paired treatments, using ordinal variables.

two paired (ranked) tests to compare number outcomes

Question 10

Incidence

Answer 10

the number of specified new events, e.g. number of people falling ill with a specified disease, during a specified period, in a specified population. people years

Question 11

Prevalence

Answer 11

the number of cases of a disease existing in a given population at a specific period of time or at a particular moment in time.

Question 12

Normally distributed data is

Answer 12

parametric

and thus conforms to a bell-shaped curve

It is symmetric about the mean and all three measures of central tendency: the mean, median, and mode are generally equal or very close in value

Question 13

Parametric tests

Answer 13

, such as Student’s t test, mandate several assumptions about the variables being considered, namely that they are normally distributed.

Parametric tests are generally considered to be more powerful than nonparametric tests (choice E) and measurements should be, at a minimum, on an interval scale.
Nonparametric data, on the other hand, is generally skewed and therefore does not fit a bell curve. Discrete data, such as that measured on a nominal or ordinal scale (choice C), consists of only a few possible values or categories and will not follow the normal distribution. It should therefore be analyzed using methods other than Student’s t test (choice D). Assessment of nonparametric data generally involves the use of contingency tables and significance testing uses methods such as the Mann-Whitney U (also known as Wilcoxon rank-sum) or Chi Square tests.

Question 14

Sensitivity describes

Answer 14

the proportion of patients who have a disease (D+) that will test positive for the disease (T+).

Question 15

The higher the specificity of a test

Answer 15

the lower the likelihood of obtaining a false-positive result. Specificity, therefore, is also equal to 1 – the false-positive rate.

This makes it a good screening test because if it says you are negative you are negative

Question 16

Positive predictive value

Answer 16

gives the percentage of patients with a positive test result who actually have the disease

low positive predictive value will have a high likelihood of producing false positives and will require confirmatory testing with a more reliable test.

calculated by dividing the number of true positives by the total number of positive test results.

Question 17

Type II errors

Answer 17

when a statistically significant difference DOES exist between the population means and yet it is not detected

and The null hypothesis (H0) is inappropriately accepted.

Question 18

The probability of committing a type II

error is estimated by

Answer 18

BETA (type 2 = b)

generally set at 0.20

(i.e., the researcher accepts a 20% chance that they will incorrectly accept the null hypothesis).

Question 19

A common reason of committing a type II error is

Answer 19

underpowered.

the study sample is too small to detect the difference that actually exists.

Question 20

Type I errors are committed by

Answer 20

This is the number one error seen in publications (because people don’t publish non significant results..)

final conclusion states that a statistically significant difference exists between the population means when, in fact, one does not

and The null hypothesis H0 is incorrectly rejected.

Question 21

The probability of

committing a type I error is predicted by

Answer 21

ALPHA (type 1 = alpha)

generally set at 0.05

(i.e., the researcher accepts a 5% chance of incorrectly rejecting the null hypothesis).