Statistics is an integral part of lean methodologies, and any leader pursuing a professional development in the lean direction should definitely take a lot of time to fully grasp all the intricate details of the field that are relevant to their job. As it turns out, not all of the statistics are directly relevant to someone working in a leadership position, but there are a few branches that are pretty much a basic requirement if you want to progress further.
One of the two main areas of statistics, this concerns the way values change over time and their effects on certain aspects of your production. There are different techniques that you can use to get a good idea of what your data means, and you’ll need to develop a good understanding of terms like mean, median, and variance.
Most of the details related to descriptive statistics are easy to learn and they just take a lot of practice, but it’s important that you develop a solid understanding of how they work fundamentally, and the motivation behind each of those methods.
In the end, you’ll probably find yourself using technology to do a lot of the heavy lifting, and you won’t have to do any complex statistical calculations yourself. But if you don’t actually understand why you’re using those formulas and calculations, and how you got to those points in the first place, you won’t get very far no matter what kind of advanced software you may have access to.
On the other side of the field, we have inferential statistics, which is concerned with uncovering relationships between different values and areas of your research. Using the techniques in this field of statistics, you can test your theories about the way the company can/should develop after certain changes, and you’ll be able to perform analysis on how new changes impact the business when compared against old data.
The important thing to understand about inferential statistics is that it allows you to get a good feeling about the current state of affairs regarding a certain parameter, without having the full data for that parameter available. In other words, you can make educated decisions about the way to move forward with just small samples of data.
This can also allow you to more effectively compare different techniques and approaches to a particular problem, by comparing small sets of their result data and evaluating the statistical differences.
Improper sampling can lead to huge inaccuracies when working with inferential statistics, and for this reason, developing a good understanding of how sampling should be performed is one of your main goals in this area. You’ll need to pick the appropriate sampling strategy, and know what kind of deviations you can expect from the specific type of data you’re currently working with.
The Best of Both Worlds
Don’t fall for the trap of thinking that one branch of statistics is more important than the other. They were both developed for specific reasons, and you should be striving to learn both areas as quickly as possible if you want to develop yourself as a lean leader quickly enough. Sure, it might benefit you more in your current work to only focus on one side of the science, but this will cause problems in the end.
Some people find themselves split between different techniques even within the same field of statistics for example, using the mean versus the median or something along those lines. To anyone with a good understanding of statistics, this argument wouldn’t even make sense it’s a bit like arguing whether the hammer or the saw is a better tool for a carpenter. Every method developed and established in the field of statistics has a very specific, concrete application and it’s important to learn all of them as best as possible.
The sooner you get started on your journey to master statistics, the better you’ll be able to develop your leadership abilities in the long run. It doesn’t matter if your actual analysis is being done by machines or other professionals in your organization, the important thing is that you, the leader, are able to fully understand what’s going on in each of those situations, and why certain decisions were made.
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