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26 Cards in this Set

  • Front
  • Back
Theory and methodology for study design and for describing, analyzing, and interpreting data.
Each member of the population has an equal chance of being chosen
Simple random sample
Population is divided into non-overlapping groups (called strata). Sample is then choses from these groups
Stratified random sample
Random sampling with a system. Starting point chosen at random and then samples are chosen at regular intervals after.
Systematic sample
Units to be sampled are chosen in clusters close to each other. Within clusters, units are chosen by simple random sampling
Cluster sample
Subjects are convenient (next 20 patients in your clinic)
Convenience sample
Data that is categorical and discrete
Qualitative data
Data that is numeric and continuous
Quantitative data
Value that occurs most frequently
-Unimodal and multimodal
Measure of the variability in scoring
Standard deviation
The square of the standard deviation
Middle of the box plot
Box contains what percentage?
25th percentile to 75th percentile
Whiskers of box plot depict?
The highest and lowest scores.
The standard deviation above or below the mean
The value calculated in a population
What can standard normal deviation do?
1. With a mean of zero and standard deviation of one
2. Allows standard scores to be calculated (z-scores)
3. Converting scores to percentile ranks
Sample size
1. Greater the sample size, more likely the distribution will look like the true distribution in a population
2. With increased sample size, the spread (variation around the mean) decreases
A single number, like the mean
Point estimate
A range of numbers that describe uncertainty due to play of chance
Interval estimate
A range of values that has a specified probability of containing the parameter being estimated
Confidence interval
Measure of correlation, Pearson's r
r = -1
r = +1
r = 0
Type I error
False Positive, when you reject the null hypothesis when it is true
Type II error
False negative, when you accept Ho when Ho is false