Business Process Improvement Plan – Making a Beginning with BPI
Business Process Improvement has gained ground in all sectors of business as an on-going program that is implemented from time to time. Periodically business organizations embrace this program to reduce the barriers that create obstacles to the work flow as well as to remove redundant processes that are outdated and do not add value to […]
Information System for Decision Support
Introduction In the current globalized business environment, decision making is becoming more and more difficult. Some of the problems faced by business are as follows: There is large volume of internal organizational data on hand thanks to the modern data-storage system. However, not all data available would be useful for decision. The flow of information […]
Business Process Ownership
Organizations depend upon Business Process Improvement to make changes to the internal operations in order to drive efficiency in the process as well as to impact the business. As the business needs keep changing, the Organizations too need to keep changing at the same speed and frequency to be able to meet up with the […]
What’s Behind the Big Tech Meltdown? Is It a Temporary Blip or a Longer Term Reckoning?
Anything That Goes Up Has To Come Down! The Big Tech Meltdown and Its Causes and Consequences Once considered the “darling” of stock markets and the world in general, it does look like the “party’s over” for Big Tech firms! From Tesla to Twitter, and from Meta to Microsoft, with Amazon and Google following suit, […]
Effects of FIFA World Cup on the Economy
The FIFA World Cup is an international sporting event. This event is held every four years. However, the host country changes every time. Countries have to bid against each other in order to win the rights to host the next World Cup. Many economists have questioned this practice. However, none have been able to find […]
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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