Miscellaneous Flashcards
central limit theorem
for large data samples, the means of many samples are normally distributed, even if the individual sample is not normally distributed
alpha
- how much overlap you are willing to tolerate
- your tolerance for making a type 1 error
- the higher your alpha, the more likely you are to make a type 1 error
- typically 5%
- can be spread over 2 tails or 1 tail (2 is stricter than 1)
p value
- probability that your result is due to chance
- want P to be less than or equal to alpha
- the smaller your P value, the less likely your results are due to chance
generalizability/external validity
- the degree to which you can extrapolate a sample to your population
- the more exclusive/tighter your inclusion criteria for subjects is, the lower the generalizability
- the looser your criteria for subjects is, the higher your generalizability
paired test
- each subject gets the intervention you are testing
- measure value before and after intervention ( each subject acts as their own control)
Does standard error apply to population or sample?
population
what does 95% confidence interval mean?
- 95% chance that the true population mean is within the mean + or - 2 SE
5 steps of hypothesis testing
1) establish hypothesis (Ho, A=B)
2) establish alpha (usually 5%)
3) do the STAT MAGIC
4) compare your p value to alpha
5) reject or fail to reject Ho
a false positive is a type ___ error
1 (alpha)
a false negative is a type ___ error
2 (beta)
when is the likelihood of making a type 1 error at its lowest?
the first time you analyze your data (each time after your chance of type 1 error increases)
If you have an intervention that you believe will only slightly differ from the placebo, would you use a large or small sample?
small
- if difference between intervention and placebo is v small, then you want dispersion to be narrow enough to minimize the overlap
- SE= SD/ sqrt(n), so if you increase n (sample size), then you decrease the dispersion of your population data, and therefore decrease the overlap
describe the x and y variables of the chi-squared/fischer’s exact test.
- categorical x variable (can have multiple)
- categorical y variable (can have multiple)
describe the x and y variables of the correlation test.
- continuous x variable
- continuous y variable
- ALL CONTINUOUS VARIABLES MUST HAVE NORMAL DISTRIBUTION*
describe the x and y variables of the t-test and ANOVA tests
- categorical x variable ( 2 for t-test, 3+ for ANOVA)
- continuous y variable
- ALL CONTINUOUS VARIABLES MUST HAVE NORMAL DISTRIBUTION*