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