The Black Curtain

Black_Curtain_and_DoorA couple of weeks ago an important event happened.  A Masterclass in Demand and Capacity for NHS service managers was run by an internationally renown and very experienced practitioner of Improvement Science.

The purpose was to assist the service managers to develop their capability for designing quality, flow and cost improvement using tried and tested operations management (OM) theory, techniques and tools.

It was assumed that as experienced NHS service managers that they already knew the basic principles of  OM and the foundation concepts, terminology, techniques and tools.

It was advertised as a Masterclass and designed accordingly.

On the day it was discovered that none of the twenty delegates had heard of two fundamental OM concepts: Little’s Law and Takt Time.

These relate to how processes are designed-to-flow. It was a Demand and Capacity Master Class; not a safety, quality or cost one.  The focus was flow.

And it became clear that none of the twenty delegates were aware before the day that there is a well-known and robust science to designing systems to flow.

So learning this fact came as a bit of a shock.

The implications of this observation are profound and worrying:

if a significant % of senior NHS operational managers are unaware of the foundations of operations management then the NHS may have problem it was not aware of …

because …

“if transformational change of the NHS into a stable system that is fit-for-purpose (now and into the future) requires the ability to design processes and systems that deliver both high effectiveness and high efficiency ...”

then …

it raises the question of whether the current generation of NHS managers are fit-for-this-future-purpose“.

No wonder that discovering a Science of  Improvement actually exists came as a bit of a shock!

And saying “Yes, but clinicians do not know this science either!” is a defensive reaction and not a constructive response. They may not but they do not call themselves “operational managers”.

[PS. If you are reading this and are employed by the NHS and do not know what Little’s Law and Takt Time are then it would be worth doing that first. Wikipedia is a good place to start].

And now we have another question:

“Given there are thousands of operational managers in the NHS; what does one sample of 20 managers tell us about the whole population?”

Now that is a good question.

It is also a question of statistics. More specifically quite advanced statistics.

And most people who work in the NHS have not studied statistics to that level. So now we have another do-not-know-how problem.

But it is still an important question that we need to understand the answer to – so we need to learn how and that means taking this learning path one step at a time using what we do know, rather than what we do not.

Step 1:

What do we know? We have one sample of 20 NHS service managers. We know something about our sample because our unintended experiment has measured it: that none of them had heard of Little’s Law or Takt Time. That is 0/20 or 0%.

This is called a “sample statistic“.

What we want to know is “What does this information tell us about the proportion of the whole population of all NHS managers who do have this foundation OM knowledge?”

This proportion of interest is called  the unknown “population parameter“.

And we need to estimate this population parameter from our sample statistic because it is impractical to measure a population parameter directly: That would require every NHS manager completing an independent and accurate assessment of their basic OM knowledge. Which seems unlikely to happen.

The good news is that we can get an estimate of a population parameter from measurements made from small samples of that population. That is one purpose of statistics.

Step 2:

But we need to check some assumptions before we attempt this statistical estimation trick.

Q1: How representative is our small sample of the whole population?

If we chose the delegates for the masterclass by putting the names of all NHS managers in a hat and drawing twenty names out at random, as in a  tombola or lottery, than we have what is called a “random sample” and we can trust our estimate of the wanted population parameter.  This is called “random sampling”.

That was not the case here. Our sample was self-selecting. We were not conducting a research study. This was the real world … so there is a chance of “bias”. Our sample may not be representative and we cannot say what the most likely bias is.

It is possible that the managers who selected themselves were the ones struggling most and therefore more likely than average to have a gap in their foundation OM knowledge. It is also possible that the managers who selected themselves are the most capable in their generation and are very well aware that there is something else that they need to know.

We may have a biased sample and we need to proceed with some caution.

Step 3:

So given the fact that none of our possibly biased sample of mangers were aware of the Foundation OM Knowledge then it is possible that no NHS service managers know this core knowledge.  In other words the actual population parameter is 0%. It is also possible that the managers in our sample were the only ones in the NHS who do not know this.  So, in theory, the sought-for population parameter could be anywhere between 0% and very nearly 100%.  Does that mean it is impossible to estimate the true value?

It is not impossible. In fact we can get an estimate that we can be very confident is accurate. Here is how it is done.

Statistical estimates of population parameters are always presented as ranges with a lower and an upper limit called a “confidence interval” because the sample is not the population. And even if we have an unbiased random sample we can never be 100% confident of our estimate.  The only way to be 100% confident is to measure the whole population. And that is not practical.

So, we know the theoretical limits from consideration of the extreme cases … but what happens when we are more real-world-reasonable and say – “let us assume our sample is actually a representative sample, albeit not a randomly selected one“.  How does that affect the range of our estimate of the elusive number – the proportion of NHS service managers who know basic operation management theory?

Step 4:

To answer that we need to consider two further questions:

Q2. What is the effect of the size of the sample?  What if only 5 managers had come and none of them knew; what if had been 50 or 500 and none of them knew?

Q3. What if we repeated the experiment more times? With the same or different sample sizes? What could we learn from that?

Our intuition tells us that the larger the sample size and the more often we do the experiment then the more confident we will be of the result. In other words  narrower the range of the confidence interval around our sample statistic.

Our intuition is correct because if our sample was 100% of the population we could be 100% confident.

So given we have not yet found an NHS service manager who has the OM Knowledge then we cannot exclude 0%. Our challenge narrows to finding a reasonable estimate of the upper limit of our confidence interval.

Step 5

Before we move on let us review where we have got to already and our purpose for starting this conversation: We want enough NHS service managers who are knowledgeable enough of design-for-flow methods to catalyse a transition to a fit-for-purpose and self-sustaining NHS.

One path to this purpose is to have a large enough pool of service managers who do understand this Science well enough to act as advocates and to spread both the know-of and the know-how.  This is called the “tipping point“.

There is strong evidence that when about 20% of a population knows about something that is useful for the whole population – then that knowledge  will start to spread through the grapevine. Deeper understanding will follow. Wiser decisions will emerge. More effective actions will be taken. The system will start to self-transform.

And in the Brave New World of social media this message may spread further and faster than in the past. This is good.

So if the NHS needs 20% of its operational managers aware of the Foundations of Operations Management then what value is our morsel of data from one sample of 20 managers who, by chance, were all unaware of the Knowledge.  How can we use that data to say how close to the magic 20% tipping point we are?

Step 6:

To do that we need to ask the question in a slightly different way.

Q4. What is the chance of an NHS manager NOT knowing?

We assume that they either know or do not know; so if 20% know then 80% do not.

This is just like saying: if the chance of rolling a “six” is 1-in-6 then the chance of rolling a “not-a-six” is 5-in-6.

Next we ask:

Q5. What is the likelihood that we, just by chance, selected a group of managers where none of them know – and there are 20 in the group?

This is rather like asking: what is the likelihood of rolling twenty “not-a-sixes” in a row?

Our intuition says “an unlikely thing to happen!”

And again our intuition is sort of correct. How unlikely though? Our intuition is a bit vague on that.

If the actual proportion of NHS managers who have the OM Knowledge is about the same chance of rolling a six (about 16%) then we sense that the likelihood of getting a random sample of 20 where not one knows is small. But how small? Exactly?

We sense that 20% is too a high an estimate of a reasonable upper limit.  But how much too high?

The answer to these questions is not intuitively obvious.

We need to work it out logically and rationally. And to work this out we need to ask:

Q6. As the % of Managers-who-Know is reduced from 20% towards 0% – what is the effect on the chance of randomly selecting 20 all of whom are not in the Know?  We need to be able to see a picture of that relationship in our minds.

The good news is that we can work that out with a bit of O-level maths. And all NHS service managers, nurses and doctors have done O-level maths. It is a mandatory requirement.

The chance of rolling a “not-a-six” is 5/6 on one throw – about 83%;
and the chance of rolling only “not-a-sixes” in two throws is 5/6 x 5/6 = 25/36 – about 69%
and the chance of rolling only “not-a-sixes” in three throws is 5/6 x 5/6 x 5/6 – about 58%… and so on.

[This is called the “chain rule” and it requires that the throws are independent of each other – i.e. a random, unbiased sample]

If we do this 20 times we find that the chance of rolling no sixes at all in 20 throws is about 2.6% – unlikely but far from impossible.

We need to introduce a bit of O-level algebra now.

Let us call the proportion of NHS service managers who understand basic OM, our unknown population parameter something like “p”.

So if p is the chance of a “six” then (1-p) is a chance of a “not-a-six”.

Then the chance of no sixes in one throw is (1-p)

and no sixes after 2 throws is (1-p)(1-p) = (1-p)^2 (where ^ means raise to the power)

and no sixes after three throws is (1-p)(1-p)(1-p) = (1-p)^3 and so on.

So the likelihood of  “no sixes in n throws” is (1-p)^n

Let us call this “t”

So the equation we need to solve to estimate the upper limit of our estimate of “p” is


Where “t” is a measure of how likely we are to choose 20 managers all of whom do not know – just by chance.  And we want that to be a small number. We want to feel confident that our estimate is reasonable and not just a quirk of chance.

So what threshold do we set for “t” that we feel is “reasonable”? 1 in a million? 1 in 1000? 1 in 100? 1 in10?

By convention we use 1 in 20 (t=0.05) – but that is arbitrary. If we are more risk-averse we might choose 1:100 or 1:1000. It depends on the context.

Let us be reasonable – let is say we want to be 95% confident our our estimated upper limit for “p” – which means we are calculating the 95% confidence interval. This means that will accept a 1:20 risk of our calculated confidence interval for “p” being wrong:  a 19:1 odds that the true value of “p” falls outside our calculated range. Pretty good odds! We will be reasonable and we will set the likelihood threshold for being “wrong” at 5%.

So now we need to solve:

0.05= (1-p)^20

And we want a picture of this relationship in our minds so let us draw a graph of t for a range of values of p.

We know the value of p must be between 0 and 1.0 so we have all we need and we can generate this graph easily using Excel.  And every senior NHS operational manager knows how to use Excel. It is a requirement. Isn’t it?


The Excel-generated chart shows the relationship between p (horizontal axis) and t (vertical axis) using our equation:


Step 7

Let us first do a “sanity check” on what we have drawn. Let us “check the extreme values”.

If 0% of managers know then a sample of 20 will always reveal none – i.e. the leftmost point of the chart. Check!

If 100% of managers know then a sample of 20 will never reveal none – i.e. way off to the right. Check!

What is clear from the chart is that the relationship between p and t  is not a straight line; it is non-linear. That explains why we find it difficult to estimate intuitively. Our brains are not very good at doing non-linear analysis. Not very good at all.

So we need a tool to help us. Our Excel graph.  We read down the vertical “t” axis from 100% to the 5% point, then trace across to the right until we hit the line we have drawn, then read down to the corresponding value for “p”. It says about 14%.

So that is the upper limit of our 95% confidence interval of the estimate of the true proportion of NHS service managers who know the Foundations of Operations Management.  The lower limit is 0%.

And we cannot say better than somewhere between  0%-14% with the data we have and the assumptions we have made.

To get a more precise estimate,  a narrower 95% confidence interval, we need to gather some more data.

[Another way we can use our chart is to ask “If the actual % of Managers who know is x% the what is the chance that no one of our sample of 20 will know?” Solving this manually means marking the x% point on the horizontal axis then tracing a line vertically up until it crosses the drawn line then tracing a horizontal line to the left until it crosses the vertical axis and reading off the likelihood.]

So if in reality 5% of all managers do Know then the chance of no one knowing in an unbiased sample of 20 is about 35% – really quite likely.

Now we are getting a feel for the likely reality. Much more useful than just dry numbers!

But we are 95% sure that 86% of NHS managers do NOT know the basic language  of flow-improvement-science.

And what this chart also tells us is that we can be VERY confident that the true value of p is less than 2o% – the proportion we believe we need to get to transformation tipping point.

Now we need to repeat the experiment experiment and draw a new graph to get a more accurate estimate of just how much less – but stepping back from the statistical nuances – the message is already clear that we do have a Black Curtain problem.

A Black Curtain of Ignorance problem.

Many will now proclaim angrily “This cannot be true! It is just statistical smoke and mirrors. Surely our managers do know this by a different name – how could they not! It is unthinkable to suggest the majority of NHS manages are ignorant of the basic science of what they are employed to do!

If that were the case though then we would already have an NHS that is fit-for-purpose. That is not what reality is telling us.

And it quickly become apparent at the master class that our sample of 20 did not know-this-by-a-different-name.

The good news is that this knowledge gap could hiding the opportunity we are all looking for – a door to a path that leads to a radical yet achievable transformation of the NHS into a system that is fit-for-purpose. Now and into the future.

A system that delivers safe, high quality care for those who need it, in full, when they need it and at a cost the country can afford. Now and for the foreseeable future.

And the really good news is that this IS knowledge gap may be  and extensive deep but it is not wide … the Foundations are is easy to learn, and to start applying immediately.  The basics can be learned in less than a week – the more advanced skills take a bit longer.  And this is not untested academic theory – it is proven pragmatic real-world problem solving know-how. It has been known for over 50 years outside healthcare.

Our goal is not acquisition of theoretical knowledge – is is a deep enough understanding to make wise enough  decisions to achieve good enough outcomes. For everyone. Starting tomorrow.

And that is the design purpose of FISH. To provide those who want to learn a quick and easy way to do so.

Stop Press: Further feedback from the masterclass is that some of the managers are grasping the nettle, drawing back their own black curtains, opening the door that was always there behind it, and taking a peek through into a magical garden of opportunity. One that was always there but was hidden from view.


Sat 5th October

It started with a tweet.

08:17 [JG] The NHS is its people. If you lose them, you lose the NHS.

09:15 [DO] We are in a PEOPLE business – educating people and creating value.

Sun 6th October

08:32 [SD] Who isn’t in people business? It is only people who buy stuff. Plants, animals, rocks and machines don’t.

09:42 [DO] Very true – it is people who use a service and people who deliver a service and we ALL know what good service is.

09:47 [SD] So onus is on us to walk our own talk. If we don’t all improve our small bits of the NHS then who can do it for us?

Then we were off … the debate was on …

10:04 [DO] True – I can prove I am saving over £160 000.00 a year – roll on PBR !?

10:15 [SD] Bravo David. I recently changed my surgery process: productivity up by 35%. Cost? Zero. How? Process design methods.

11:54 [DO] Exactly – cost neutral because we were thinking differently – so how to persuade the rest?

12:10 [SD] First demonstrate it is possible then show those who want to learn how to do it themselves.

We had hard evidence it was possible … and now MC joined the debate …

12:48 [MC] Simon why are there different FISH courses for safety, quality and efficiency? Shouldn’t good design do all of that?

12:52 [SD] Yes – goal of good design is all three. It just depends where you are starting from: Governance, Operations or Finance.

A number of parallel threads then took off and we all had lots of fun exploring  each others knowledge and understanding.

17:28 MC registers on the FISH course.

And that gave me an idea. I emailed an offer – that he could have a complimentary pass for the whole FISH course in return for sharing what he learns as he learns it.  He thought it over for a couple of days then said “OK”.

Weds 9th October

06:38 [MC] Over the last 4 years of so, I’ve been involved in incrementally improving systems in hospitals. Today I’m going to start an experiment.

06:40 [MC] I’m going to see if we can do less of the incremental change and more system redesign. To do this I’ve enrolled in FISH

Fri 11th October

06:47 [MC] So as part of my exploration into system design, I’ve done some studies in my clinic this week. Will share data shortly.

21:21 [MC] Here’s a chart showing cycle time of patients in my clinic. Median cycle time 14 mins, but much longer in 2


21:22 [MC] Here’s the same clinic from patients’ point if view, wait time. Much longer than I thought or would like


21:24 [MC] Two patients needed to discuss surgery or significant news, that takes time and can’t be rushed.

21:25 [MC] So, although I started on time, worked hard and finished on time. People were waited ages to see me. Template is wrong!

21:27 [MC] By the time I had seen the the 3rd patient, people were waiting 45 mins to see me. That’s poor.

21:28 [MC] The wait got progressively worse until the end of the clinic.

Sunday 13th October

16:02 [MC] As part of my homework on systems, I’ve put my clinic study data into a Gantt chart. Red = waiting, green = seeing me


16:34 [SD] Hurrah! The visual power of the Gantt Chart. Worth adding the booked time too – there are Seven Sins of Scheduling to find.

16:36 [SD] Excellent – good idea to sort into booked time order – it makes the planned rate of demand easier to see.

16:42 [SD] Best chart is Work In Progress – count the number of patients at each time step and plot as a run chart.

17:23 [SD] Yes – just count how many lines you cross vertically at each time interval. It can be automated in Excel

17:38 [MC] Like this?



This is the work-in-progress chart. The most useful process monitoring chart of all. It shows the changing size of the queue over time.  Good flow design is associated with small, steady queues.

18:22 [SD] Perfect! You’re right not to plot as XmR – this is a cusum metric. Not a healthy WIP chart this!

There was more to follow but the “ah ha” moment had been seen and shared.

Weds 16th October

MC completes the Online FISH course and receives his well-earned Certificate of Achievement.

This was his with-the-benefit-of-hindsight conclusion:

I wish I had known some of this before. I will have totally different approach to improvement projects now. Key is to measure and model well before doing anything radical.

Improvement Science works.
Improvement-by-Design is a skill that can be learned quickly.
FISH is just a first step.

A Treaty with the Lions

This week I heard an inspiring story of applied Improvement Science that has delivered a win-win-win result. Not in a hospital. Not in a factory. In the red-in-tooth-and-claw reality of rural Kenya.

Africa has vast herds of four-hoofed herbivors called zebra and wildebeast who are accompanied by clever and powerful carnivors – called lions. The sun and rain make the grass grow; the herbivors eat the grass and the carnivors eat the herbivors. It is the way of Nature – and has been so for millions of years.

Enter Man a few thousand years ago with his domesticated cattle and the scene is set for conflict.  Domestic cattle are easy pickings for a hungry lion. Why spend a lot of energy chasing a lively zebra or wildebeast and run the risk of injury that would spell death-by-starvation? Lions are strong and smart but they do not have a social security system to look after the injured and sick. So why not go for the easier option?

Maasai_WarriorsSo Man protects his valuable cattle from hungry lions. And Man is inventive.  The cattle need to eat and sleep like the rest of us – so during the day the cattle are guarded by brave Maasai warriors armed with spears; and at night the cattle are herded into acacia thorn-ringed kraals and watched over by the boys of the tribe.

The lions come at night. Their sense of smell and sight is much better developed than Man’s.

The boys job is to deter the lions from killing the cattle.

And this conflict has been going on for thousands of years.

So when a hungry lion kills a poorly guarded cow or bull – then Man will get revenge and kill the lion.  Everyone loses.

But the application of Improvement Science is changing that ancient conflict.  And it was not done by a scientist or an animal welfare evangelist or a trained Improvementologist. It was done by young Maasai boy called Richard Turere.

He describes the why, the what and the how  … HERE.

Richard_TurereSo what was his breakthrough?

It was noticing that walking about with a torch  was a more effective lion deterrent than a fire or a scarecrow.

That was the chance discovery.  Chance favours the prepared mind.

So how do we create a prepared mind that is receptive to the hints that chance throws at us?

That is one purpose of learning Improvement Science.

What came after the discovery was not luck … it was design.

Richard used what was to hand to design a solution that achieved the required purpose – an effective lion deterrent – in a way that was also an efficient use of his lifetime.

He had bigger dreams than just protecting his tribe’s cattle. His dream was to fly in one of those silver things that he saw passing high over the savannah every day.

And sitting up every night waving a torch to deter hungry lions from eating his father’s cattle was not going to deliver that dream.

So he had to nail that Niggle before he could achieve his Nice If.

Like many budding inventors and engineers Richard is curious about how things work – and he learned a lot about electronics by dismantling his mother’s radio! It got him into a lot of trouble – but the knowledge and understanding that he gained was put to good use when he designed his “lion lights”.

This true story captures the essence of Improvement Science better than any blog, talk, lecture, course or book could.

That is why it was shared by those who learned of his improvement; then to TED; then to the World; then passed to me and I am passing it on too.  It is an inspiring story. It says that anyone can do this sort of thing if they choose to.

And it shows how Improvement Science spreads.  Through the grapevine.  And understanding how that works is part of the Science.

The Power of the Converted Skeptic

puzzle_lightbulb_build_PA_150_wht_4587One of the biggest challenges in Improvement Science is diffusion of an improvement outside the circle of control of the innovator.

It is difficult enough to make a significant improvement in one small area – it is an order of magnitude more difficult to spread the word and to influence others to adopt the new idea!

One strategy is to shame others into change by demonstrating that their attitude and behaviour are blocking the diffusion of innovation.

This strategy does not work.  It generates more resistance and amplifies the differences of opinion.

Another approach is to bully others into change by discounting their opinion and just rolling out the “obvious solution” by top-down diktat.

This strategy does not work either.  It generates resentment – even if the solution is fit-for-purpose – which it usually is not!

So what does work?

The key to it is to convert some skeptics because a converted skeptic is a powerful force for change.

But doesn’t that fly in the face of established change management theory?

Innovation diffuses from innovators to early-adopters, then to the silent majority, then to the laggards and maybe even dinosaurs … doesn’t it?

Yes – but that style of diffusion is incremental, slow and has a very high failure rate.  What is very often required is something more radical, much faster and more reliable.  For that it needs both push from the Confident Optimists and pull from some Converted Pessimists.  The tipping point does not happen until the silent majority start to come off the fence in droves: and they do that when the noisy optimists and equally noisy pessimists start to agree.

The fence-sitters jump when the tug-o-war stalemate stops and the force for change becomes aligned in the direction of progress.

So how is a skeptic converted?

Simple. By another Converted Skeptic.

Here is a real example.

We are all skeptical about many things that we would actually like to improve.

Personal health for instance. Something like weight. Yawn! Not that Old Chestnut!

We are bombarded with shroud-waver stories that we are facing an epidemic of obesity, rapidly rising  rates of diabetes, and all the nasty and life-shortening consequences of that. We are exhorted to eat “five portions of fruit and veg a day” …  or else! We are told that we must all exercise our flab away. We are warned of the Evils of Cholesterol and told that overweight children are caused by bad parenting.

The more gullible and fearful are herded en-masse in the direction of the Get-Thin-Quick sharks who then have a veritable feeding frenzy. Their goal is their short-term financial health not the long-term health of their customers.

The more insightful, skeptical and frustrated seek solace in the chocolate Hob Nob jar.

For their part, the healthcare professionals are rewarded for providing ineffective healthcare by being paid-for-activity not for outcome. They dutifully measure the decline and hand out ineffective advice. Their goal is survival too.

The outcome is predictable and seemingly unavoidable.

So when a disruptive innovation comes along that challenges the current dogma and status quo, the healthy skeptics inevitably line up and proclaim that it will not work.

Not that it does not work. They do not know that because they never try it. They are skeptics. Someone else has to prove it to them.

And I am a healthy skeptic about many things.

I am skeptical about diets – the evidence suggests that their proclaimed benefit is difficult to achieve and even more difficult to sustain: and that is the hall-mark of either a poor design or a deliberate, profit-driven, yet legal scam.

So I decided to put an innovative approach to weight loss to the test.  It is not a diet – it is a design to achieve and sustain a healthier weight to height ratio.  And for it to work it must work for me because I am a diet skeptic.

The start of the story is  HERE

I am now a Converted Healthier Skeptic.

I call the innovative design a “2 out of 7 Lo-CHO” policy and what that means is for two days a week I just cut out as much carbohydrate (CHO) as feasible.  Stuff like bread, potatoes, rice, pasta and sugar. The rest of the time I do what I normally do.  There is no need for me to exercise and no need for me to fill up on Five Fruit and Veg.


The chart above is the evidence of what happened. It shows a 7 kg reduction in weight over 140 days – and that is impressive given that it has required no extra exercise and no need to give up tasty treats completely and definitely no need to boost the bottom-line of a Get-Thin-Quick shark!

It also shows what to expect.  The weight loss starts steeper then tails off as it approaches a new equilibrium weight. This is the classic picture of what happens to a “system” when one of its “operational policies” is wisely re-designed.

Patience, persistence and a time-series chart are all that is needed. It takes less than a minute per day to monitor the improvement.

Even I can afford to invest a minute per day.

The BaseLine© chart clearly shows that the day-to-day variation is quite high: and that is expected – it is inherent in the 2-out-of-7 Lo-CHO design. It is the not the short-term change that is the measure of success – it is the long-term improvement that is important.

It is important to measure daily – because it is the daily habit that keeps me mindful, aligned, and  on-goal.  It is not the measurement itself that is the most important thing – it is the conscious act of measuring and then plotting the dot in the context of the previous dots. The picture tells the story. No further “statistical” analysis is required.

The power of this chart is that it provides hard evidence that is very effective for nudging other skeptics like me into giving the innovative idea a try.  I know because I have done that many times now.  I have converted other skeptics.  It is an innovation infection.

And the same principle appears to apply to other areas.  What is critical to success is tangible and visible proof of progress. That is what skeptics need. Then a rational and logical method and explanation that respects their individual opinion and requirements. The design has to work for them. And it must make sense.

They will come out with a string of “Yes … buts” and that is OK because that is how skeptics work.  Just answer their questions with evidence and explanations. It can get a bit wearing I admit but it is worth the effort.

An effective Improvement Scientist needs to be a healthy skeptic too – i.e. an open minded one.