First of all, I would like to set the concept of Hypothesis Testing then we will move step by step to the agenda of this post. Hypothesis testing, what is it? It is a method of drawing inference or conclusion about the sample based on a population as a whole. In simpler words, the process of collecting samples from a population and to determine whether the results are statistically significant or not.

You might be wondering “Why we use t-test specifically and why not other tests?” Because in most of the process scenarios, we do not know the population parameters like mean or standard deviation and hence the only option is t-test.

Suppose in a Pharmaceutical Company, they manufacture lakhs of tablets per day – in such case, it is difficult to know the population parameters like mean and standard deviation. So we apply sample t-tests.

According to the National Health profile 2018 released by Central Bureau of Health Intelligence, it was reported that the current doctor-population ratio is 1:11,082 in India. The current statistics is drastically lower than the ratio of 1:1000 as recommended by WHO.

Now, coming to the blog – we will discuss data science and “How data science can improve present healthcare sector in India?”

Let us first have a brief overview of Data Science – When we look to a few years back where traditional health information system (HIS) was largely applied. Patient information was recorded on sheets and everything was manual.

A simple demonstration of Histogram usage in your process!

Histogram is commonly used for graphical representation of numerical data. Most importantly, it is one of the basic quality improvement tools used in various industrial processes.

The term Quality generally defined as “fitness for intended use”. It plays an important role in organisational excellence. Improving quality has become a top priority in an organisation, whether in discrete, process manufacturing or service industry – achieving customer satisfaction is the highest level of quality. So, we should apply techniques which can help to gain detailed insights into customer requirements and ability to provide services or products to meet or exceed their expectations.

Minitab offers several quality tools to help you explore and detect quality problems and improve your process in an objective, quantitative way. To improve quality, our common goals should include reducing defect rates, manufacturing products within specifications, standardizing delivery time and so on.

Since we are living in a data-driven society, where Data Analytics have taken centre stage in recent years. Data Analytics helps us to extract valuable information from data and support decision making strategy in an organisation. The importance of data analytics is very well known in all areas. We use data analytics to find new improvement opportunities in an organisation irrespective of industrial types. It is applicable in all the department of an organisation viz. manufacturing, sales, finance, marketing, production, supply chain, R&D and so on.

Have you ever come across the term “Biostatistics” before? I guess maximum will tell yes and minimum will tell no or vice versa. Relax, I will leave it to you.

Biostatistics is a branch of Statistics which deals with the data related to Medical Science, Public Health, Medical Research, Epidemiology and so on. In simpler words, when we apply Statistics in Biological Science – it is called Biostatistics.

Quality is the common terminology which has become one of the most important customer decision factors and thus controlling and improving quality has become the topmost priority in an organisation. For an organisation to sustain in today’s competitive world, it should deliver quality consistently.

Quality can be generally defined as a “fitness for intended use”. As per the manufacturing perspective, to manufacture products based on the quality of conformance (design specifications). Manufacturing covers a wide range of areas like aviation, automobiles, pharmaceutical, electronics, food & beverages, chemical, glass and so on. As we all know that manufacturing quality is a continuous improvement process and every one of an organisation, starting from ground level to a higher level, has to contribute their knowledge & experience to achieve the highest level of quality.

I guess this is an important question we should ask ourselves before doing any kind of analysis. We should know the sample size required to do the analysis. Say, if we collect more samples than required – what will happen? We will waste our resources and time. Thus enhanced cost and complexity. On the other hand, if we collect fewer samples than our test may fail and hence less accuracy on results. On an extreme note, our test may fail to detect the important effect and could not meet the test objective. Hence we should determine the optimum sample size required for analysis.

Suppose in an organisation, when we have to evaluate the quality of incoming or outgoing products – “How we do it?” Based on certain quality characteristics – “How we make a decision whether to accept or reject the lot?”

The answer lies behind the concept of “Acceptance sampling”. It is an alternative to 100% inspection. Here, inspection is performed on a lot or batch of parts or products. We can use acceptance sampling to accept or reject a lot based on a representative sample. It plays a major role in the quality control process.

Please keep in note that acceptance sampling doesn’t improve the quality of a process but it helps to control quality to a great extent. It helps in decision making whether to accept or reject lots.

In this competitive world, Quality has become the pivotal factor which differentiates an organisation from its competitors. And most importantly one of the most important customer decision factors in the selection of various products and services. By providing good quality services, we can meet customer expectations, enhance organisational’s image, increase profitability, reduce costs, meet industry standards and so on. Thus, focusing on quality helps keep an organisation strong in all areas.

Quality can be generally defined as a “fitness for intended use”. As per the service perspective, to provide an excellent service without any interventions and to meet or exceed customer expectations. In other words, quality is a value for a performance or a service.

Service sector covers a wide range of areas like hospitality, healthcare, IT, Finance, marketing & sales, retail, e-commerce, media and so on. For an organisation to sustain in today’s competitive world, it should deliver quality consistently. As we all know that service quality is a continuous improvement process and every one of an organisation, starting from ground level to a higher level, has to contribute their knowledge & experience to achieve the highest level of quality.