Statistics is the science of collecting and analysing numerical data in order to infer meaning from the data.
Statisticians exist for a reason, statistics is complicated and attempting to understand the entire breadth of statistics would be unrealistic and near on impossible. Papers should look to present data using statistical methods that are explicit and attempts to answer the question being posed as simply as possible.
There are some statistical concepts that are referred to frequently in papers that we would benefit from understanding and this section will focus on those.
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Once you’ve listened help consolidate the topics covered with the questions below.
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The presence of bias in a study can lead to inaccurate results?
Precision and accuracy mean the same thing?
A lack of precision can be demonstrated by the presence of wide confidence intervals?
An inaccurate result will have wide confidence intervals?
Prevalence relates to the number of people in the population who have the disease of interest at one particular time?
Incidence relates to the number of people with a disease at a point in time in the population?
The hypothesis is the relationship that the researchers are attempting to prove?
A p-value reflects the likelihood that the null hypothesis is it true?
The narrower the confidence interval the more precise the result is?
False positive’s are referred to as type 2 errors?
False negatives study results are termed type 2 errors?
Power calculations help researchers reduce the chance of type 2 errors and provide sample sizes required for the study?
By decreasing the magnitude of the clinical outcome you wish to prove you improve the power of the study?