Easy Statistical Tests for Analysing Data

The most common ways to analyse data you've gathered from measuring results

So you've got your numbers, how many people, how much was done, using what resource; now you have to report it in a meaningful manner.
This is where Statistics fits in
If you compare two numbers, e.g. the number of users before the new service was introduced and the number after, then you can get one of three results:
MoreThe second number is higher than the first
The SameNo Change
Less
This doesn't tell you whether you could expect the same change if you took measures a second time; for example you might have more people one week completely at random.
To counter this, you can use statistics, which estimate the 'likelihood' that one number is different from another, or the likelihood (H0 or Null Hypothesis) that they are the same.
Of course you need to go back to What constitutes success, or the benefits outlined in the Benefits Approach. To make the reports meaningful, you need to decide what they will demonstrate - 40 graphs and charts pinned to a notice board are pretty meaningless.
For example, if you want to show the service has improved and you've collected:
numbers of people using the service each day
average wait from arrival to being seen
average wait from appointment time to being seen
period from referral to appointment
patient satisfaction
follow-up or repeat appointments as a proportion of total day's appointments
number of that day's appointments that result in a follow- up
'see and treat' resolution of problem that day
which of these would show an improved service?
None! Comparing the figures with the week before, the month before, the year before would show if the service was improving or not (though it's important to remember to use run charts or averages - figures comparing one day to another are counter-productive).
Of course you also need enough clinical knowledge to know what direction of change represents an improvement - follow- up appointments may be good, and they may represent failure.
How do you analyse the data
Plot the dots
The first thing to do is to understand your data. What does it look like?
Is it "normal" or not?
parametric or non-parametric?
Does the data tell you what you need to know, or do you need to process it in some way before it will?
We'll explore Basic descriptive statistics and simple statistical tests in the next page