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FINER method
 1) feasible
 2) interesting
 3) novel
 4) ethical
 5) relevant

positive controls
those that ensure a change in the dependent variable when it is expected

Negative controls
ensure no change in the dependent variable when no change is expected

confounding variables
error that results when a causal variable is associated with two other variables but is not accounted for; may falsely say two variables are associated

observational studies
can be cohort: those in which subjects are sorted into two groups based on differences in risk factors and then assessed at various intervals
crosssectional studies: attempt to categorize patients into different groups at a single point in time
casecontrol studies: start by identifying the number of subjects with or without a prticular outcome

Hill's criteria
describe the components of an observed relationship that increase the likelihood of causality in the relationship
1) temporality: exposure must occur before the outcome (independent before dependent)
2) strength: as more variability in outcome is explained by variability in study variable, the realtionship is more likely to be causal
3) doseresponse relationship:as the study or independent variable increases, there is a proportional increase in the response; the more consistent it is, the more likely it is to be causal
4) consistency: the relationship is found ot be similar in mutiple settings
5) plausibility: reasonable mechanism for the independent variable to impact the dependent variable supported by literature
6) consideration of alternate explanations: remaining explanation is more likely
7) experiment; if experiment can be performed, a causal relationship can be determined
8) specificity: change in outcome varibale is only produced by an associated change in independent variable
9) coherence: new data and hypothesis are consistent with the current state of scientific knowledge

detection bias
results from educated professionals usign their knowledge in an inconsistent way

selection bias
subjects are not representative of the target population

observation bias
hawthorne effect: people chang ebehavior because they are being studied

internal validity
suppor for causality

external validity
generalizability: ability to apply findings to another population
low generalizability: sample not reflect target populaton
high: samples represent target population

statisticalyl significant
not result of random chace

clinical significance
practical importance of a treatment effect  whether it has a real genuine, palpable, noticeable effect on daily life.

internal validity
ability to infer causality from a study or replicate its results under the same conditions

skewed distribution
one that has a tail on one side of the data site

negatively skewed distribution
mean is lower than median and tail is to the left

positively skewed distribution
mean is higher than median adn tail is to the right

interquartile range
 first quartile: multiply n by 1/4
 > if a whole number: the quartile is the mean of the value at this position and the next highest position; if it is a decimal, round up to the next whole number and take that as the quartile position
 third quartile: multply n by 3/4
 > follow same instructions
IQR=Q3Q1

outlier
any values that fall more than 1.5 IQRs below first or above third quartile

Standard deviation
 1) first, determine value of mean
 2) find diffrerence between each data point and mean and then square the value
 3) add the squared values and divide by n1
 4) take square root of that

null hypothesis
always a hypothesis of equilance

alternative hypothesis
nondirectional or directional

ttests
rely on the standard distribution or the closely related tdistribution
1) a test statistic is calculated and compared to a table to determine the likelihood that that statistic was obtained by random chance: called our pvalue

What do we do with our pvalue
compare it to a significance level (alpha)
 p > α: we fail to reject null hypothesis, meaning there is not a significant differenc ebetween the two populations
 p < α: we reject the null hypothesis and state that there is a statistically significant difference between the two groups
if the alternative hypothesis is not directional, compare our pvalue to α/2

type I error
value of α is the level of risk that we are willing to accept for incorrectly rejecting the null hypothesis
likelihood that we report a difference between two populations when one does not exist

type II error
occurs when we incorrectly fail to reject the null hypothesis
likelihood that we report no difference between two populations when one actually exists
sympbolized by Beta

confidence intervals
determine range of values from the sample mean and standard deviation
we begin with a desired confidence level (ex: 95%) and use a table to find corresponding z and t score; when we multiply from teh mean, we create a range of values and can then say we are 95% confident that the sample falls between those two values

