Module 5 Flashcards
What is internal validity?
Findings are free of chance, bias and confounding. IS there something making the study findings wrong?/Are there other explanations for the results?
What is a parameter?
True values of the measure in the population
What is an estimate?
The value found in the study sample
What happens if you repeatedly sample randomly from the same population?
Most of the time you would get a sample with a similar composition to the population you sampled from but some of the samples would be quite different just by chance. This is sailing error which is a form of random error and is commonly just called chance
What can reduce sampling error?
Increasing sample size
What is external validity?
- whether findings can be applied to broader populations
- generalisability
- judgement call about what has being studied applies to
How is a confidence interval interpreted?
We are (confidence level), that the parameter lies between (lower bound) and (upper bound)
What is interpreted first?
Measure of association and then the confidence interval
What happens if the whole CI is above the clinically important value?
Then the study findings are clinically importance
What happens if part of the CI is above the clinically important value?
The study findings may have clinical importance
What happens if the CI excludes the clinically important value?
The study findings have no clinical importance
What are p values?
The probability of getting a study estimate (or one further from the null), when there really is no association due to sampling error. If p is really low then it is unlikely the estimate is due to sampling error
What is the null hypothesis?
There is no association and the parameter = null value
What is the alternative hypothesis?
there is an association and the parameter does not equal the null value
How is a p value interpreted?
The probability fo finding a (sample statistic) of (value) or further from the null when the null hypothesis is true is (p-value)
What is set for a type 1 error?
A threshold which is usually 0.05
What happens if the p value is less than 0.05?
Since the p-value is less than 0.05 the association is statistically significant. We reject the null hypothesis and accept the alternative hypothesis. Chance is an unlikely explanation of the study finding
What happens if the p value is more than 0.05?
Since the p-value is more than 0.05 the association is not statistically significant. We fail to reject the null hypothesis and reject the alternative hypothesis. The study finding is consistent with chance as an explanation
what is a type 2 error?
When we incorrectly fail to reject the null hypothesis when should have (p should have been less than 0.05 but it was more)
What is a type 2 error typically due to?
Having too few people in the study
What can statisticians do?
Calculate power to find out how many participants are needed to minimise chance of a type 2 error
Why are p values problematic?
Arbitrary threshold, only about the null hypothesis and nothing about importance
What is meant by and arbitrary threshold?
Sometimes it isn’t too far from 0.05 so the p-value should be reported as well as saying it is statistically significant or not statistically significant
What is meany by it is only about the null hypothesis?
It just gives evidence about consistency with the null hypothesis nothing about precision which is why they are best presented with confidence intervals
