In an earlier article I discussed how the binomial distribution can be used to estimate parameters and make inferences. But there are many situations where the binomial distribution isn’t right for the problem at hand. In this article we’ll discuss why you shouldn’t use the binomial distribution as your primary statistical tool when analyzing a sample. (If you want to know more about using the binomial distribution to analyze data, see my previous article: “Statistical Analysis by Means of Binomial Distribution”.)

One of the most common reasons why the binomial distribution isn’t the best statistical tool to use is when you are examining a sample from a prior study. Suppose you are analyzing data from the birth records of 100 families. Each family’s birth certificate comes with information about the parents, the grandparent and every other person living in the household during the years the child was born. The binomial distribution tells you the probability that each member of the family will live to a certain age, which is based on the numbers you observe. However, if the binomial distribution was used instead of a normal distribution you would have to examine the full data set in order to calculate the binomial normality.

You see, the binomial distribution isn’t normally distributed because it doesn’t follow a normal distribution. Rather than being normal, it follows a binomial curve. Therefore, there is no way to calculate the normal distribution strength of a binomial distribution in its entirety. This means that the binomial distribution doesn’t have a strong value of the probabilities (i.e. it is bell-shaped).

If you use a binomial distribution calculator you will notice that the result that you are given will be very different from what you expect if you use the binomial normal distribution calculator. Most binomial distribution calculators will give you the range of results that is actually distributed so that you can see how it can be sampled from the natural logit. But if you use the binomial, normal distribution calculator you will get a very non-traditional distribution.

In the binomial distribution calculator you get an outcome which is the mean of the log(x) and the standard deviation of the log(x) mean. Because of the non-traditional form of distribution this outcome can have a variety of interpretations. For instance, the binomial logit may be a power spectrum or a simple binomial tree. It could also be a binomial closed range over finite values, or it could be a binomial random variable over some finite range.

But this isn’t the entire power of binomial distribution calculators. What binomial distribution calculators lack in is the ability to interpret the results as they are produced by the model. Many people don’t realize that this is an important capability, because without it there is no way to actually simulate what would happen under various probability scenarios. This is why binomial distribution calculator can sometimes come with other features which can help to interpret the results as they are produced by the binomial distribution.

One example of this is the simulation of the jackknife distribution. Most binomial distribution calculators will produce results which are based on simulated data, but the binomial closed range can give you more realistic results. These include simulated data for different prior distributions, the binomial bell curve, and the binomial exponential. All these features make it possible to simulate different results with ease and confidence.

When looking at your return, whether it’s from stocks, bonds, mutual funds or lottery winnings, you need to ask yourself how well you’ve chosen each investment. This is where a binomial distribution calculator can help you make the right decisions. With all these benefits of using binomial distribution calculator, there should be no reason why you shouldn’t try it out today.