Evidence Based-Approaches: Concepts of Biostatistics Flashcards
Descriptive Statistics
Process of describing data; taking raw data and providing summarizing info or depicting data through figures
Sample
Small subset of a population
Population
The entire group that you want to draw conclusions about
Inferential Statistics
Builds on descriptive statistics and allows researchers to draw conclusions based on info collected from the sample
Variables
Characteristics that is of most interest of the subjects
Statistic
A summarizing characteristic of sample’s variable
Parameters
Summary attributes of a population
Nominal Variables
Categories of the variable that have no order
Dichotomous Variables
Variables that have only 2 possible variables
Ordinal Variables
Categories of the variables that have an inherent order
Continuous Variables
Variables that can take any value between a min and max value
Interval Variables
Variables that have a distinct order and clearly defined intervals; lack a true 0. Also fails to reveal ratios of amounts.
Ratio Variables
Variables that have a distinct order and clearly defined intervals; have a true 0. Variables act as true ratios of one another
Frequency Tables
Used for nominal and ordinal data. Give information on frequency, relative frequency, cumulative frequency, and cumulative relative frequency.
Measures of Central Tendency
Gives single values that describe the entire data for continuous variables
Mean
Arithmetic average of the data. The sum of total values divided by the number of data values
Median
Middle value of the data set
Mode
Most common value of the data set
Measures of Variability
Provides information on the spread of the data set
Variance
Standard deviation squared
Standard Deviation
A measure of how dispersed the data is in relation to the mean
25% Quartile
Value between the median and lowest value
75% Quartile
Value between the median and highest value
Interquartile Range
75% Quartile -25% Quartile
Range
Spectrum of values between highest and lowest value
Probability
The numerical value applied to the likelihood for the occurrence of an event. All are proportions and range between 0 and 1.
Conditional Probability
Assessing the probability of a characteristic given another characteristic
Indepedence
A circumstance when the probability of one event does not have an impact on the probability of another event.
Binomial Distribution
Model of the distribution of a dichotomous outcome variable
Normal Distribution
Gaussian Distribution; model of the distribution of a continuous outcome variable
Parametric Tests
Tests that depend on assumptions about the underlying distribution
Normal Distribution Key Characteristics
- mean, mode and median are all the same and located in the center of the distribution
- distribution is symmetric and not skewed
- theoretical range extends horizontally from positive infinity to negative infinity
- Only 2 parameters are necessary to describe a normal distribution: standard deviation and mean
- area under the curve is exactly 1
Central Limit Theorem
With repeated sampling, the individual mean calculations of samples form a normal distribution
Null Hypothesis
Precise Statement; H0
Alternative Hypothesis
More ambiguous statements that rivals the null hypothesis
P-values
Probability values that measure the likelihood of obtaining the observed statistic or more extreme values when the null hypothesis is true
Level of Significance
Alpha; Benchmark for rejection
One-sided Test
Sample deviating from the null hypothesis conditions in one specific direction
Two-sided Test
Does not specify directionality of sample
Reject the Null Hypothesis
Less than alpha
Fail to Reject the Null Hypothesis
Greater than alpha
One Sample Z-Tests
Form of hypothesis testing that is performed on a continuous response variable.
Degrees of Freedom
Number of observations - x
Type I Error
Rejecting a true null hypothesis
Type II Error
Failing to reject a false null hypothesis (what researchers prefer)
