# Why the null hypothesis (Ho) should never be ‘accepted’?

People may have some level of difficulty in making inferences using hypothesis testing.

Let me put forth a common mistake that we do while making conclusions.

We know that when the p-value is less than the level of significance (α), we reject the null hypothesis. Commonly we take α as 0.05. So when p< 0.05, we reject the null hypothesis.

But, when the p> 0.05 we can not reject the null hypothesis.

At this point, many of us tend to say that ‘we accept the null hypothesis‘ and this statement is not correct.

Reason:

We make conclusions about the population based on a random chance that could say that Ho is true. But this random chance is at the chosen level of α. There is a possibility that some other randomly chosen sample gives a different conclusion at a different level of significance.

Thus, when p> α, we fail to reject Ho

When p is greater than α we should write the results in neutral language. For example,

Ho: Machine 1 is producing spindles with average length of 30 cm

Ha: Machine 1 is not producing spindles with average length of 30 cm

If p< 0.05, At the significance level of 0.05, we reject the null hypothesis that machine 1 is producing spindles with an average length of 30 cm. This also means that we accept (and not prove) the alternate hypothesis,  a nice explanation can be found here for this case.

When p> 0.05, at the significance level of 0.05, we can not reject the null hypothesis that machine 1 is producing spindles with an average length of 30 cm.

This simply means that at a significance level of 0.05, we can not conclude whether the machine is producing an average length of 30 cm or not.

So, we should always keep in mind that when we are not able to reject the null hypothesis, we should not conclude in any other way but in a neutral manner.

Do share in comments, your experience and other mistakes that you may have once committed/ witnessed.

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# Sampling Basics

There are certain vital techniques which must be understood well if we want to understand the subject of statistics and measurements. Sampling is one such very important topic, which we’ll be covering in a couple of articles.

## Some terminologies first…

To understand the concept, we should also understand a few frequent terms.

• Element– an object on which a measurement is taken
• Population– a collection of elements about which we wish to make an inference
• Sampling units non-overlapping collections of elements from the population that cover the entire population
• Sampling frame– a list of sampling units
• Sample- a collection of sampling units drawn from a sampling frame
• Parameter: numerical characteristic of a population
• Statistic: numerical characteristic of a sample

## What is Sampling?

The activity in which elements, from a population, are collected so as to represent the population. In this video, a very good introduction to sampling has been provided.

## Why Sampling?

Most of the times, the population is too large to measure all of its elements, thus sampling is done. A sample reflects the characteristics of the population from which it is drawn. For example, a machine produces 1000 spindles a day. It may be difficult to measure all of them, so we take samples and measure them.

It is very crucial that samples are selected carefully. Incorrect sampling may lead to incorrect inferences about the population.

Sampling has many advantages over exhaustive sampling, which covers the whole population.

• Sampling can save money
• Sampling can save time
• In case of destructive inspection, it is not prudent to do exhaustive sampling

In our next article, we’ll learn about the common sampling techniques.

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# The 8 wastes of Lean

A lean manufacturing framework, which emphasises on the idea of providing value to the customer in less work, was given by Taiichi Ohno, the father of Toyota Production System. He suggested that anything that doesn’t add value in the eye of the customer is a waste (“Muda” in Japanese) and every effort should be made to eliminate that waste. The following 8 components of waste, though initially introduced in Lean Manufacturing system, have universal application; be it any industry or domain.

There are two famous acronyms used to remember these 8 wastes- one is TIMWOODS (Transportation, Inventory, Motion, Waiting, Overproduction, Over-processing, Defects, Skill’s underutilization). The other is DOWNTIME, which stands for Defects, Overproduction, Waiting, Non-utilized skills, Transportation, Inventory, Motion and Extra processing.

Find a good detailed explanation of each of this type of waste here.

# What is Six sigma?

If as a beginner in Six Sigma journey you are confused about exactly what is Six Sigma, you have come to the right place!

Six Sigma is a tool, methodology, a metric, a measure, a benchmark, a goal, a philosophy and a statistical term.

Let us see, how does ‘Six sigma’ fit into all these roles?

As a statistical term: 6σ is a statistical term in which σ is a Greek letter which denotes standard deviation. Standard deviation represents the variation of the process or the process spread around mean. A Six sigma process is a process in which the 6 standard deviations above or below mean are within the nearest specification limits.

In the above figure, the orange curve has high spread, i.e. high standard deviation; while the blue curve has lower standard deviation as compared to the orange one. In general terms, we can say process having blue curve is better since it is more clustered around the mean.

Let’s also consider the two vertical black lines now, which are the upper and lower specification limits. In this case, the blue curve is well within the specification limits. If we also assume that the 6σ spread above and below the mean are within these specification limits, the blue process is a Six Sigma process. In a six sigma process, there is a scope of 1.5σ shift towards upper/ lower specification limit.

As a Tool: 6σ is a set of existing tools used for business performance improvements, to design new processes & products, improve existing processes & product.

As a Methodology: 6σ comprises of methodologies like DMAIC, DMADV, DFSS etc. The most common methodology used for improving existing processes/ products is DMAIC, which stands for five phases of a 6σ project, Define, Measure, Analyse, Improve & Control.

As a Measure: We use Sigma Level as a measure to state the capability of the process. As soon the sigma level increases (which means that more of the process spread squeezes between specification limits) the probability of getting defects decreases. A 6σ process has a probability of getting only 3.4 defects per million opportunities.

As a Metric: There are various metrics which are used in a six sigma project and serve various purposes. Ex: Defects per unit (DPU), Defects per million opportunities (DPMO), Rolled Throughput Yield (RTY), First Time Yield (FTY) etc.

As a Benchmark: 6σ level companies are world-class benchmarks. Their processes are so effective and efficient that the cost of poor quality and probability of defects is very low.

As a Goal: An organisation can make a Goal to attain the 6σ level. Though reaching to this high level of sigma is considered very challenging than to attain 2 sigma level, where simply doing standardisation can help. As sigma level goal increases, the complexity increases and more sophisticated tools are required to attain such a high capability.

As a Philosophy: Six Sigma, if followed religiously, brings a cultural change. Like it has changed the DNA of GE — “it is now the way we work — in everything we do and in every product we design.”

In whatever form you understand Six sigma, adopting six sigma brings a cultural change across the organization, with use of some simple and some sophisticated tools, in a systematic and data-driven approach to bring out the continuous improvements.

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# 6 steps to listen to your customer effectively

We all know it very well that the Customer is King, but when it comes to figuring out what to do that our King stays happy, at times we fall short of ideas. An organization’s sole duty is to provide value for his money, by meeting all his requirements.  All the requirements that he tells (explicit) and even all that he doesn’t tell (implicit).

Finding Voice of the customer is as vital as delivering him with a good product or service. But is it a one-time exercise? Does it end at the beginning when we gather our customer’s requirements?

Along with VOC, there are various tools and techniques to help us understand customers’ requirements, but unless we go in a logical and systematic way, we can’t have the momentum going so as to keep the customer happy for long.

As a short term measure, we can collect the feedback and work upon that, but this would not remove the causes behind the dissatisfaction.

Making use of the concept of continual improvement, below steps can help to ensure that the King does stay happy all the time.

1. Empathise with every customer:

Put yourself in your customer’s shoes! Put the needs of the customer first, have service orientation, learn to listen actively to the customer, apologise for the discomfort caused to him, stay polite and sincere while trying to resolve his complaint.

Perhaps we can even go that extra mile by buying our own product/ availing own’s service and see what is the experience.

1. Put effective feedback tools:

OK, you do that satisfaction survey with your customer, but does that really give you the picture crystal clear? Even after survey results show you have done well, the financial results may or may not match! As an organization, you must not wait for the results of customer satisfaction survey, instead keeping eyes and ears open for every sign that customer is not happy is very important.

Since getting unsolicited feedback is very rare, it is important to put an effective feedback mechanism in place.

Create a mechanism that the customer has the liberty to give feedback as and when he feels to do so.

You may make use of various tools like feedback boxes, direct contact and exploratory interviews, analytics methods, usability tests, and surveys. But keep a rider that these are available to the customer as soon as he has got something to tell.

1. Immediate redressal of complaints (and responding to suggestions):

It is going to have a way worse ride if you do not act upon your customer’s feedback, and that too immediately! Never wait for too long. Buy some time from your customer by acknowledging as soon as you receive a complaint and providing an estimated time to closure with a reason. And yes, don’t forget to act upon the complaint.

It is not always necessary that you only get complaints. Even if there is a suggestion, your customer expects an exchange of appreciation that he took his time out to help you. Ask your customer service desk to be as prompt in acknowledging the suggestions as they do for the complaints.

1. Understand and analyse the customer complaints & put preventive measures in place:

It is good that you have an effective mechanism in place to address and correct customer complaints. But more important in the long run is that you analyse the nature of the complaints and the root causes behind.

Customer complaints are always not a sign that something is wrong. You need to filter out such false alarms from your complaint database while doing analysis.

When you analyse the data, you come up with the root causes. Now your next target should be to remove those causes so that you can prevent future complaints and issues leading to dissatisfaction.

1. Monitor customer satisfaction trends:

You need to check regularly that whether the measures that have been taken are effective or not. It is very critical to have a monitoring and review mechanism in place so that timely actions could be taken if something is going wrong.

1. Improve & sustain:

Correction, prevention and monitoring are going to help you improve your product or service quality. Subsequently, it becomes very important that control measures are put in place which should help in effectively sustaining the improvements else, the whole exercise goes for a toss and you may miss the opportunity to satisfy your King in the long run.

And, don’t forget the positive feedbacks:

If a customer is happy, that should also be analysed. You should not be just analysing what went wrong, you should also assess what went well that customer is happy and bring those good practices in front of everyone and make them standard practices as suitable.

Regardless of the industry, type of work, product or service, not listening to the customer is self-destructive. However, if we make use of a systematic and logical methods, I’m sure, our King would stay happy always.

Would love to hear your views, as a lot of points may have got missed here.