Intro to Biostatistics Flashcards
Dependent Variable
Outcome you are measuring or looking for
Independent Variable
What is manipulated/changed during an experiment or study
Null Hypothesis (H0)
States there will be no true difference between the groups being compared
Alternative Hypothesis (H1)
States there will be a true difference between the groups being compared
Nominal Grouping
Dichotomous/binary; non-ordered, named categories; no order or magnitude, no consistency of scale or equal distances; simply labeled variables without quantitative characteristics
Ordinal Grouping
Ordered, rank-able categories; non-equal distance; they have order/magnitude but have NO consistency of scale or equal distances
Interval/Ratio Grouping
Ordered, magnitude, and equal distances/units; have order/magnitude AND consistency of scale/equal distances
Ex: Living siblings (number) and personal age (in years)
Interval: Arbitrary zero value (but 0 doesn’t mean absence)
Ratio: Absolute rational zero value (0 DOES mean absence of measurement value)
Which groups are considered “discrete” data?
Nominal and Ordinal
Which groups are considered “continuous” data?
Interval/Ratio
Mode
Most common number
Median
Middle number after numbers are placed in order
Mean
Average of all numbers
Minimum, Maximum, Range
Minimum = lowest Maximum = highest Range = difference between min and max
Variance
Average of the squared differences in each individual measurement value (x) and the groups’ mean
Standard Deviation
Square root of variance value
When a dataset is normally distributed, which values are equal or near equal?
Mean and Median
1 standard deviation is what percentage under the curve?
68%
2 standard deviations is what percentage under the curve?
95%
3 standard deviations is what percentage under the curve?
99.7%
Positively Skewed
When mean is higher than median; tail is pointing to the right
Negatively Skewed
When mean is lower than the median; tail is pointing to the left
Kurtosis
Measure of the extent to which observations cluster around the mean; for a normal distribution, the value of the kurtosis statistic is 0
Positive Kurtosis = more cluster
Negative Kurtosis = less cluster
Skewness
Measure of the asymmetry of a distribution
Required assumptions of Interval/Ratio data for the proper selection of a parametric test
- Normally distributed
- Equal variances (multiple tests available for equal variances between groups)
- Randomly-derived and independent
Levene’s Test
Test that tells us if data is normally distributed or not and if it has equal variances
How do you handle data that is NOT normally-distributed?
Use a statistical test that does not require the data to be normally distributed, such as ordinal or nominal tests, or transform data to a standardized value with the hope that this transformation allows data to be normally-distributed (may not work)
Type 1 Error
NOT accepting the null hypothesis when it is actually true and should have been accepted; there really is no true differences between the groups; also called “alpha”
Type 2 Error
Accepting the null hypothesis when it is actually false, and you should NOT have accepted it; there really IS a true difference between the groups being compared; also called “beta”
Power
1-beta; statistical ability of a study to detect a true difference, IF one truly exists between group-comparisons, and therefore the level of accuracy in correctly accepting/not accepting the null hypothesis