Process Optimisation Management:
Part 4.2 — Analyse Phase

4 min readMar 7, 2021

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Today we will look at mathematical hypothesis testing. The purpose of appropriate hypothesis testing is to integrate the Voice of the Process with the Voice of the Business to make data-based decisions to resolve problems.

Hypothesis testing always analyses two alternatives and our mathematical analysis will determine which answer is correct. Imagine your overall problem is the low quality of customer satisfaction with your coffee in your café. One of your Xs is that different coffee bean suppliers cause statistical differences in the taste of your coffee.

Then your first hypothesis H0 would say:

H0: There is no difference in taste depending on which coffee bean supplier you choose.

The alternative hypothesis H1 would however say following:

H1: There is a difference in taste depending on which coffee bean supplier you choose.

In order to run a mathematical analysis, you need to have the coffee being tasted for a certain period of time and being rated by your customers (for optimal results on a 5–7 point scale). At the same time you need to write down which coffee bean was used when a customer rates the coffee.

The mathematical analysis will help you to answer following questions:
How many samples do I have to take? Which variables out of the collected data will I use to make my judgement? How much different do my results have to be to disprove H0 or H1?

Just imagine, supplier A beans show an average customer satisfaction of 4.3, supplier B beans yield an average customer satisfaction of 4.5 and supplier C beans produce an average customer satisfaction of 4.7.

Would you be able to say with certainty and without mathematical help that supplier A is significantly better? Is a difference of 0.2 enough to call it already “better”? And what does average actually mean? In a worst case scenario half of your customers loved it (7) and half of it hated it (rating it 1.6). The average would be 4.3, but in reality you don’t want 50% of your customers ruining their morning by drinking your coffee. As you see, you will also have to look at the so called variance of the data collected. And how reliable are customer ratings? Imagine you would ask your customers again tomorrow and then the day after that? Would they always give the same answer or would they also have some variances in their answers. I personally know that my ratings are always better Friday afternoon than Monday morning.

To answer all these questions, we are using something called inferential statistics. Inferential statistics is the cool brother of descriptive statistics which you are using in Excel. Bar or pie charts would be examples of descriptive statistics and all they do is describing data.

Inferential statistics however, as Wikipedia puts it, is “the process of using data analysis to infer properties of an underlying distribution […] Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates.”

Life is a bit more complicated

In the above example we assumed that there was a linear connection between supplier and coffee taste. But in reality there would be a group of Xs interacting with each other to create the taste of the coffee. Imagine your barista Betty is amazing in working the machine while Michele is not as good. And your machine is also producing probably better results after having been cleaned in the morning. So in this example you have three interacting variables (barista, time and coffee bean suppliers) which influence the taste of your coffee. These interacting effects can also be modelled with inferential statistics.

If your models get to become too complicated and no one in your company is able to run these analyses, try to get help early on to get this right. Because identifying the potential vital few root causes of your problem will later on boost the results of your A.I. outsourcing partner.

As the old saying goes: “Good data in, good data out”.

After you have found your vital few root causes for your problem and mathematically proved their vitality, you are finally ready to call your outsourcing partner. You are well equipped to have your first A.I. scoping workshop to see, how A.I. can help you improve your problems. In the next chapter (s.index below), we are going to secure your successes and show the world, how amazingly you worked.

If you are interested in A.I. leadership education or want to start your A.I. journey, just contact me at ansgar_linkedin@goldblum-consulting.com and I will help you prepare your company to be ready to start your A.I. journey.