Most people are confused by statistics and because of this experts often regard them as ignorant, stupid or both. However, those who claim to be experts in statistics need to proceed with caution – and here is why.

The people who are confused by statistics are confused for a reason – the statistics they see presented do not make sense to them in their world. They are not stupid – many are graduates and have high IQ’s – so this means they must be ignorant and the obvious solution is to tell them to go and learn statistics. This is the strategy adopted in medicine: Trainees are expected to invest some time doing research and in the process they are expected to learn how to use statistics in order to develop their critical thinking and decision making. So far so good, so what is the outcome?

Well, we have been running this experiment for decades now – there are millions of peer reviewed papers published – each one having passed the scrutiny of a statistical expert – and yet we still have a health care system that is not delivering what we need at a cost we can afford. So, there must be someone else at fault – maybe the managers! They are not expected to learn or use statistics so that statistically-ignorant rabble must be the problem -so the next plan is “Beat up the managers” and “Put statistically trained doctors in charge”.

Hang on a minute! Before we nail the managers and restructure the system let us step back and consider another more radical hypothesis. What if there is something not right about the statistics we are using? The medical statistics experts will rise immediately and state “Research statistics is a rigorous science derived from first principles and is mathematically robust!” They are correct. It is. But all mathematical derivations are based on some initial fundamental assumptions so when the output does not seem to work in all cases then it is always worth re-examining the initial assumptions. That is the tried-and-tested path to new breakthroughs and new understanding.

The basic assumption that underlies research statistics is that *all measurements are independent of each other* which also implies that order and time can be ignored. This is the reason that so much effort, time and money is invested in the design of a research trial – to ensure that the statistical analysis will be correct and the conclusions will be valid. In other words the research trial is designed around the statistical analysis method and its founding assumption. And that is OK when we are doing research.

However, when we come to apply the output of our research trials to the Real World we have a problem.

How do we demonstrate that implementing the research recommendation has resulted in an improvement? We are outside the controlled environment of research now and we cannot distort the Real World to suit our statistical paradigm. Are the statistical tools we used for the research still OK? Is the founding assumption still valid? Can we still ignore time? Our answer is clearly “NO” because we are looking for a change over time! So can we assume the measurements are independent – again our answer is “NO” because for a process the measurement we make now is influenced by the system before, and the same system will also influence the next measurement. **The measurements are NOT independent of each other**.

Our statistical paradigm suddenly falls apart because the founding assumption on which it is built is no longer valid. We cannot use the statistics that we used in the research when we attempt to apply the output of the research to the Real World. We need a new and complementary statistical approach.

Fortunately for us it already exists and it is called **improvement statistics** and we use it all the time – unconsciously. No doctor would manage the blood pressure of a patient on Ward A based on the average blood pressure of the patients on Ward B – it does not make sense and would not be safe. This single flash of insight is enough to explain our confusion. There is more than one type of statistics!

New insights also offer new options and new actions. One action would be that the Academics learn improvement statistics so that they can understand better the world outside research; another action would be that the Pragmatists learn improvement statistics so that they can apply the output of well-conducted research in the Real World in a rational, robust and safe way. When both groups have a common language the opportunities for systemic improvment increase.

BaseLine© is a tool designed specifically to offer the novice a path into the world of improvement statistics.