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38 Cards in this Set

  • Front
  • Back
interval data
data with a numerical value, absolute difference between 2 values can alway be determined by subtraction
nominal or categorical data
data not measured on an interval scale (non-numeric), such as gender, state of birth or presence of disease
ordinal data
data which is categorical, but has inherent ordering. Example: level of health (excellent, good, fair, poor)
dispersion about the mean. measured as the average squared deviation from the mean. variance=sum of (value associate with member of population-population mean)^2/number of population members
standard deviation
square root of the variance (square root of the average squared deviation from the mean)
normal (Gaussian) distribution
bell-shaped curve. ~68% of population within 1 SD, 95% within 2 SDs
stratified random sample
population divided in subpopulation (strata) prior to random sampling
systematic difference between the characteristics of the sample and the population
two ways to obtain data
experimental and observational
sample standard deviation (equation)
s=√(Σ〖(X-X ̅ )〗^2/n-1)
standard error of the mean (SEM), (xbar subscript xbar)
standard deviation of all possible sample means, measures the uncertainty in the estimate of the mean
As the sample size from the population increases, the standard error of the mean (SEM) ______.
The more variable the total population is the standard error of the mean (SEM) _______.
Term which states:
-normal distribution of sample means indepent of the the original population
-mean value of all sample means=mean of original population
-SD of all possible means of samples (SEM) depends on both the SD of the original population and the sample size.
Central Limit Theorem
the value that half the population falls below, 0.5(n+1) observation
25th percentile point (lowest quartile) formula
0.25 (n+1) observation
interquartile range
the interval between the 25th and 75th percentile points
percentile which corresponds to mean + 0.67 standard deviation (in a normal distribution)
75th percentile
Percentile which corresponds to mean + 1 standard deviation (in a normal distribution)
84th percentile
Percentile which corresponds to mean + 2 standard deviations
97.5the percentile
Null hypothesis
Hypothesis that there is no effect introduced by a treatment
Analysis of variance
Class of related procedure to test for differences between groups
Parametric statistical methods
Procedures comparing groups based on population parameters within normal distribution (i.e. mean, standard deviation)
Non-parametric statistical methods
Procedures comparing groups based on frequencies, ranks or percentiles
Formula for variance within the treatment groups
s_within^2=1/4(s_control^2+s_(treatment 1)^2+s_(treatment 2)^2+s_(treatment 3)^2),
S^2 is variance, for study with a control and 3 treatment groups
If the null hypothesis is true, what is the relationship between the within-groups variance and between-groups variance?
About equal (both are estimates of the same population variance).
About equal (both are estimates of the same population variance).
F=population variance estimated from sample means/population variance estimated as average of sample variances (F=s_between^2 / s_within^2)
What is a “big” F?
There is a larger than expected variability within the samples, so rejection of the null hypothesis that all the samples were drawn from the same population. Report a P-value < 0.05.
What is single factor or one way analysis of variance?
Analysis of variance with one factor distinguishing different experimental groups.
Degree-of-freedom parameters
Numerator =Number of samples (m) minus 1, Denominator = number of samples (m) times 1 less than the size of each sample. Vn=m-1. Vd=m(n—1)
t ratio formula
t= difference in sample meand/standard error of difference of sample means, or
pooled variance estimate
s^2=1/2(s^2_one + S^2_two)
two-tailed t test
Statistical test in which extreme values of t that lead us to reject the null hypothesis lie in both tails of the distribution (i.e. both ends of the bell curve)
When determining the critical values of t (either calculating or using a table) what information must be known?
Degrees of freedom (Ʋ). This is determined by sample size n. Ʋ=2(n-1).
How is the t-test and analysis of variance related?
They t-test is simply a special case of analysis of variance applied to two groups.
When the experimental design involves multiple groups should a t-test or analysis of variance be used?
Analysis of variance. T-test is designed only for 2 group analysis.
What is the Bonferroni t test?
First perform an analysis of variance to test the overall null hypothesis. then use a multiple-comparison procedure to isolate the treatment(s) producing the different results.
What is the Bonferri inequality formula?
αT < kα, or αT/k < α. k is the number of statistical tests. Cut-off value is α. (i.e. combined t-test cut-off value is no more than k times α.