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# Understanding the Difference Between Long and Short Term Sigma

# Fishing and Hypothesis Testing!

# “p-value” What the Hell is it?

# Conceptualizing “Distribution” Will Help You in Understanding Your Problem in a Much Better Way

# 7QC Tools: Why do we Require to Plot X-bar and R-charts Simultaneously

# Is it Difficult for you to Comprehend the Concept of Confidence Interval? Try this out

# Why Standard Normal Distribution Table is so important?

# Why Do Pharmaceutical Industry Requires Quality by Design (QBD)

Hypothesis testing is a procedure of estimating the average weight of all fishes (not possible to calculate till we take out all the fishes = population) in the pond based on the data obtained from the sample of fishes caught for purpose. Since hypothesis testing is based on the sample data hence, there would be some inherent error in estimating the population parameter. Because of this error we would get an interval instead of a particular value for the population parameter.

In business decisions when we want to compare two processes, we have a theoretical limits represented by α and a corresponding experimental value represented by p-value. If p-value (experimental or observed value) is found to be less then the α (theoretical value), then two processes are different.

You will be surprised that we all are aware of this concept of distribution and we all are using it intuitively all the time! Don’t believe me? Let me ask you a simple question, to which income class do you belong? Let’s assume that your answer is middle income class. On what basis did you made this statement? Probably in your mind you have following distribution of income groups and based on this image in your mind, you are telling your position on this distribution that you belong to the middle income group. When we say that my child is not good at studies, you are drawing a distribution of all students in your mind and implicitly trying to tell the position of your child towards the left of that distribution. Whenever we talk of adjectives like rich, poor, tall, handsome, beautiful, intelligent, cost of living etc., we subconsciously, associate a distribution to those adjective and we just try to pinpoint the position of a given subject onto this distribution.

The main purpose of the control charts is to monitor the health of the process which, is done by monitoring both, accuracy and the precision of the process. Control chart for mean is used for studying the accuracy of the process whereas the control chart of variability for the precision of the process.

If we assume that the population average is a golden fish that we want to catch from the pond using our fishing net (equivalent to CI). Then we would be successful in 95 attempt out of 100.

There are business problems where we need to comprehend the probability of some events to occur based on the historical data or from the sample data. A possible way is to draw the probability density function (PDF) and calculate the area under the curve (AUC) between any two points. But for every data we encounter, it is not possible to do that. What we can do is to convert the given data into the standard normal distribution, for which the AUC between any two point is well documented, thereby providing the desired probability.

QbD is of paramount importance for the patient safety but there is another side of the coin. QbD is also required for timely and uninterrupted supply of medicines into the market. This timely uninterrupted supply is required to fulfill the 4A’s (acceptability, affordability, availability and accessibility) requirement of any Regulatory body as it is their main KRA and this required to be percolated down to the manufacturer clearly. It will help in developing a strong customer-supplier relationship between the two parties and can trigger the smooth acceptance of QbD by generic players.

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