Statistics Vs Probability

To many, probability and statistics are synonymous. While they have much in common, they do have some significant differences. When these differences are fully appreciated, you will develop a perspective that allows you to fully appreciate how you can make the most of a probabilistic approach.

While there are a number of differences, the key difference focuses upon how we acquire systemic information:

• In a statistical approach, which is probably the most common approach, we would use experimentation and the collected data to improve our understanding of the quality we would expect in the system that we are considering. The key problem with this approach is that the actual system is required for experimentation. This means we need to have built a prototype or perhaps have even started production before we can perform these experiments. In the first case, much of the design works needs to have been completed, and in the second case, we would have already finalised the design and stared production. In either case, we would have made a considerable investment before we would know if we were going to be able to meet our quality target.

• In a probabilistic approach, we start with an analytical model and use this for probabilistic analysis. By taking this approach the quality can be predicted, and even optimized, before we commit to a final design or manufacturing. This can save considerable cost and time. In particular, we can determine if a design will even be capable of providing the quality we want, and rejecting it if it can’t.

The advantage of the probabilistic approach over the statistical is clear. However, it does require an analytical model and this can be overwhelming for some. Still, you should always try developing an analytical model and analyzing the model for quality before committing to take a design further. Paradoxically, there will be times when you will need to perform an experiment(s) to develop the model so it should not be thought that the probabilistic approach is a complete replacement of the statistical. This is especially the case when dealing with quality control as opposed to product development.

 In summary, a probabilistic approach allows us to be more predictive than does the statistical. Therefore, the probabilistic approach should be used whenever possible, but it should be noted that the statistical approach is still essential. Also, we need to bring our focus onto the development of an analytical model so that we can apply probabilistic methods.