Exam I Flashcards
What is a Variable?
A variable is any characteristic that can and does assume different values for different people, objects, or events being studied
Nominal
Numbers are simply used as a code to represent characteristics.
There is no order to the categories.
Ordinal
Numbers represent categories that can be placed in a meaningful numerical order (e.g., from lowest to highest).
There is no information regarding the size of the interval between the different values.
The size of the interval may be different between the different categories.
There is no “true” zero.
Interval
Numbers can be placed in meaningful order.
The intervals between the numbers are equal.
It is possible to add and subtract across an interval scale.
There is no true zero, so ratios cannot be calculated.
Ratio
Numbers can be placed in meaningful order.
The intervals between the numbers are equal.
There is a “true” zero, determined by nature, which represents the absence of the phenomena.
Almost all biomedical measures (weight, pulse rate, and cholesterol level) are of ratio scale.
Central tendency
most frequently occurring values
Dispersion
how the values are spread out
Shape and skewness
symmetry or asymmetry of the distribution of the values
Outliers
unusual values that do not fit the pattern of the data
Standard deviation
The average distance of each point from the mean
Interquartile range
Difference between 75th and 25th percentile values.
Symmetrical Distributions
Data are evenly distributed about the center.
There is the same amount of data on the right and left side of the distribution.
Not all symmetrical distributions are “normal.”
Skewed Distributions
Data are not evenly distributed about the center.
Can be “right skewed” or “left skewed”
population standard deviation
of a population data set of N entries is the square root of the population variance.
deviation
deviation of an entry x in a population data set is the difference between the entry and the mean μ of the data set.
Interpreting Standard Deviation
Quantifies deviation from mean of a typical observation.
If data are more tightly clustered = lower SD.
If data are more spread out = higher SD
Empirical Rule
For data with a symmetric, bell-shaped distribution, the standard deviation has the following characteristics.
~ 68% of data lie within 1 SD of mean.
~ 95% of data lie within 2 SD of mean.
~ 99.7% of data lie within 3 SD of mean.
Standard error of the mean
Standard deviation / square root of (sample size)
Confidence Intervals
A range that we are confident includes the true value
Interquartile Range
The interquartile range (IQR) of a data set is the difference between the third and first quartiles.
Interquartile range (IQR) = Q3 – Q1.
Fractiles
numbers that partition, or divide, an ordered data set.
Percentiles
divide an ordered data set into 100 parts. There are 99 percentile values: P1, P2, P3…P99.
Deciles
divide an ordered data set into 10 parts. There are 9 decile values: D1, D2, D3…D9.
P-value
proportion of the null distribution less extreme than the sample value
Lower p-value = more extreme
Paired t-test
Used when you have two related groups and want to compare their means (e.g., pre- and post-intervention scores for the same individuals).
Independent t-test
Compares the means of two unrelated groups (e.g., male vs. female weight loss).
ANOVA
Used when comparing the means of three or more unrelated groups. ANOVA looks at the variance within groups (how much scores within a group differ) and the variance between groups (how much the group means differ). The ratio of these variances gives you the F-statistic, which is used to assess significance.
