Statistics Flashcards
Accuracy
- how close the measurement is to the true value
- compared to a gold standard
alpha error
probability of Type I error
beta
type II error
bias
outcome differs from the correct answer
in a systematic non-random way
biased studies
- subjects in group 1 differ from subjects in group 2
- in a meaningful way that will affect the conclusions
cohort
its a group with common characteristics
confidence interval
- The 95% confidence interval defines a range of values that you can be 95% certain contains the population mean. With large samples, you know that mean with much more precision than you do with a small sample, so the confidence interval is quite narrow when computed from a large sample.
- the true parameter (such as the mean) is expected to fall within this range
- Most commonly, the 95% confidence level is used
- Factors affecting the width of the confidence interval include
- the size of the sample,
- the confidence level,
- the variability in the sample.
- A larger sample size normally will lead to a better estimate of the population parameter.
- is a range of likely values for the population parameter based on: the point estimate, e.g., the sample mean
counfounding variable or factor
- when 2 variables are related to a 3rd variable
- one might or might not know the factor is related to the 2 principle variables
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Dependent variable
- its the outcome or effect
- for example visual acuity
independent variable
null hypothesis
there is no significant difference between specified populations, any observed difference being due to sampling or experimental error.
normal
its a Gaussian or bell-shaped curve distribution
power
probability of finding a true difference
regression
how much a dependent variable Y changes based on changes of the independent variable
type I error
- if we reject the null hypothesis when in fact it is true
- its alpha error
- we reject the null hypothesis if p<0.05
- so the groups are different
- but in reality, they are not
type II error
- its the beta error
- when we accept the null hypothesis when in fact, it is FALSE
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what are the 2 ways of missinterpreting p value?
and what to do to avoid missinterpretation?
- a p value >0.05
- thought to be non-significant
- “no effect” or “no difference”
- THE CORRECT interpretation should be:
- The is no strong evidence that the intervention has an effect
- no strong evidence that there is a difference
- THE CORRECT interpretation should be:
- ALWAYS CHECK THE P VALUE WITH THE CI
what to look for in the Confidence interval?
- you want a NARROW RANGE
- the wider the range, the worse
- the RESULT falls within that range with X% of conficence