Crackdown on Indian Credit Rating Firms
Credit rating industries are part of a closely held industry. For years, this has worked in favor of these agencies since they have to face less competition. However, these agencies are also the first ones to get blamed after every financial crisis. It is a known fact that no one can really predict a market […]
Failure Mode and Effects Analysis (FMEA)
All products and services fail! Even the processes which have already achieved six sigma precision fail. However considering the possible sources of failures, the effects that they are likely to have and how prioritizing failure modes makes the product, service, process plan more robust. Considering possible failure modes and integrating them into the design goes […]
Business Process Improvement – Project Implementation
Business Process Improvement projects are undertaken by Organizations from time to time to improve their internal performance and operational efficiencies. Success of BPI project depends largely upon two factors: First and foremost, the BPI project depends upon its sponsors who should ideally be drawn from the Management and the Business Unit Heads. Sponsorship and facilitation […]
The Sino American Trade War: Forced Technology Transfers
For the past few weeks, it seemed like America and China will finally be able to resolve their trade disputes. This is because Donald Trump and Xi Jinping were participating in a conference where a trade deal was supposed to be worked out. The markets were looking forward to some positive news. This was evidenced […]
Internal Technology Framework: 7S Framework
Introduction In the modern age of cutting-edge technology and continuous innovation, product life cycle is ever shortening. There is constant pressure on companies to differentiate from competition and earn customer satisfaction. In such a business environment, it is essential that internal organization network is strong and efficient to deal with any kind of changes. The […]
The P-Value is a statistical representation of the likelihood that the null hypothesis is true. Therefore the P-Value is the probability that the output (Y) will not change as a result of the variation that we are deliberately introducing in the input (X).
Example: If we have a null hypothesis that says that there is no statistically significant relationship between the efficiency of workers in the New York factory as compared to the one in Sacramento California, then the P value attaches a probability to this statement.
If we say that for testing the following hypothesis we assign a P-Value of 0.05. This means that we are saying that 95 out of a 100 times, the efficiencies will not be different. Therefore we are admitting that 5% of the times they could be different. In this case the null hypothesis will only be rejected if we prove that differences in efficiencies arise let’s say 7% of the times.
The P-Value is an important part of the hypothesis problem. Changing the P-Values slightly can change which hypothesis is selected and which is rejected. Therefore the selection of the P-Value must be carefully done. There are different types of errors associated with choosing the wrong P Value. These errors have been described later. The management must decide which error they can afford to make before selecting the P-Value.
The P-Value and confidence interval are intertwined. In fact, if you have the value of one you can automatically derive the value of the other. The formula used in P value = 1 - Confidence Interval. Therefore for a P Value of 0.05, the confidence interval is 0.95 or 95%.
Statistical conclusions cannot be taken literally. One needs to carefully understand how to interpret them before any decisions are made which are based on them. For instance, we stated above that the null hypothesis stands true 95% of the time. Does this mean that if we were to conduct a 100 experiments right away, the null hypothesis would stand true 95 times or more? Well, not really.
Although this is what is ideally expected to happen, the entire subject of Statistics is based on the law of large numbers. This means that these conclusions can be tested for validity only after 1000’s of trials have taken place. When the numbers are sufficiently large, 95% will hold true!
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