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epidemiologic research focuses on possible asoication
Independent variables variables set/determined by investigator
dependent variables the effects that depend upon the independent variables
Qualitative description, word not number
Quantitaive use rigid, continuous measurement scale
the order of different type of data (from low to high) nominal ==> binary (dichotomous) ==>ordinal (ranked) ==> continuous (dimensional) ==> ratio
Which three types of data are discrete data nominal, binary, ordinal
can lower order data be expanded into high order date? No. Only the higher order data contains more information cn collapsed into lower order data
the importance of statistic in clinical setting it helps us predict what might happen in the future of our patients
normal distribution the trendline of population data which exhibit "bell-shaped"
example of non-parametric data nominal, binary (dichotomous), ordinal
Parametric data data that can be easily described by "mean, mode and median"
parameter data that are characteristics of that population that help to describe or define the population
pencentiles percentage of observation below the indicated point when all the observations are ranked in descending order. Mean = 50th percentile
a measure of dispersion, lowest to highest value range
measure of variability of data about the mean (sum of squared deviation from the mean) variance
square root of variance-a smaller number used to describe the amount of "spread" in the frquency distribution standard deviation (SD)
horizontal stretching of a frequency distribution to one side or the other ==> create a long, "thin" tail of the data distribution Skewness
Vertical stretching or flattening of the frequency distribution Kurtosis
type of hypothesis testing which proceed form general to specific Deductive
Type of hypothesis testing proceeds from the specific to the general Inductive
: there is statistically significant difference between 2 groups alternative hypothesis
: there is statistically significant difference between 2 groups null hypothesis
is the probably of incorrectly reject H0 when it is actually correct. alpha
the probably of incorrectly “fail to reject” (accept) H0 when it is actually incorrect beta
: the probably that 2 group are different with respect to the data measurement p value
what is p-value when we say the difference is statistical significant p<0.05
what is p-value when we say the difference is statistical insignifciant p>=0.05
alpha is what type of error Type I error
Beta is what type of error Type II error
what is the conventional value for alpha 0.005
what is the formula for statistic power 1-beta
how do we calculate 95% Confidence interval (CI) mean+/- (1.96 x Standard error)
what can we say about two population if 95% CI does not overlap for the populations we can be "95% certain" that 2 populations are difference.
What can we say about 2 population if 95% CI does overlap we are not 95% certain that the populations are different. Therefore there is no statistically significant different between the outcomes of 2 populations
What does 95% CI tell us about a data the interval of data that include 95% of data measure
if value 1 fall in the 95% CI of RR, what is the conclusion? there is no statistically significant difference in 2 population
If value =1 does not fall in the 95% of RR, what is the conclusion about 2 population? there is Statistically significant difference between 2 populations
When RR =1, what does it tell us about the 2 populations? RR=1, Risk 1 = Risk 2, no difference between 2 population
Created by: powerbaby