# How to Use Statistics in the Most Efficient Way Possible

Post in Statistics

The Influence of Statistics

The pressure of having a higher accuracy in research statistics is something that has been increasing every day for quite some time now.

There is just so much on the line, it has become almost unheard of to get inaccurate readings. These statistics are used to directly feed information to large businesses and corporations which is why such high accuracy is called for in every department to begin with.

In order to achieve everything the required amount of precision, it becomes necessary for people to use statistics in the best way possible. This may seem like a trivial task, but it is far from it. There are plenty of nuances that you need to take into account however in order to make this happen.

The central task in the field of statistics has always been about finding the relationship of the data you are deciphering with the task hand and vice versa. This way, more can be understood about the problem given to you and thus progress can be made in the right direction.

Statistical experts have been trying to find a fixed set of rules that can be used to guide people on their journey to using statistics in the most efficient manner possible. However, it is not something that can be simply listed out and then followed for the rest of your life. There are plenty of things to experience and analyze before something of this magnitude can be achieved.

Regardless, there are certain rules that you can apply in general to make things easier for yourself. Let’s list out some of them so that you too can know what this is all about.

One of the most common misconceptions that newbies have with regards to this subject is that the procedure is everything. If there is a certain task given to them, most people will confine themselves within the boundaries of answering which method will get them to their destination faster. While there is definitely room for such a way of thinking as well, it is flawed to say the least.

What you should be concerned with is finding out how the given data can answer the scientific question that lies underneath the problem at hand. This is how understanding about a subject comes to you as a person. If you want to make progress in this field, then this understanding is key as it will facilitate you to make your way into other areas of this subject as well.

While this is applicable in other fields of work as well, it applies even more so in statistics. If you are working with data (presumably really large amounts of data), it is advisable that you make some plans way ahead. This way, you won’t get overwhelmed when the time comes that you need to work with every data set all at once without any seemingly distinct boundaries separating them.

Not only that, there are plenty of benefits of planning ahead in statistics:

• Makes data analysis much simpler to perform
• Also makes it far more rigorous and dependable
• Efficiency of handling the data also increases

Keeping your data organized is one of the first lessons that you are taught when being introduced in this subject. After all, this entire field revolves around finding the different patterns between the data. The only way you can effectively go about doing that is by arranging the data in a proper manner and planning way ahead with them.

Data Quality

Another thing that you must take care of is the quality of data that you are working with. More often than not, the initial data set given to you won’t necessarily be the most optimized one. It should be your job to convert the un-optimized data set into an optimized version so that the other operations that you want to perform on it is much easier.

This pre-processing is extremely important in statistics so much so that the final outcome of your analysis is also dependent on it. If you can do this step really well, the rest of your job becomes much easier compared to how it would have been without it.

The thing is, most of the times a good chunk of the data is not needed at all in your working operations which is why filtering them out is the best option. Even if you don’t take word for this, when you get to experience this on your own, you will understand exactly why this as true as it can get. Data quality is really important in statistics and as such, separating the good from the bad is absolutely necessary.

Simplicity is Key

Another really underrated advice that experienced people often forget to give to newbies is that no matter what happens, simplicity is key to everything. The idea is to make things as efficient as possible. You do not have to concentrate on making things fancy by applying really complicated algorithms or using obscure filtering techniques. As long as you can get the job done in the easiest way available, you’re good to go.

As the complexity of the problem increases, so will the complexity of the problem model. But your attempt should always be to keep this at a minimum. Sometimes, it is unavoidable but if you can help it, do go for that. There are few things as irritating as doing a task in a needlessly complex manner when there is a simpler solution available.

If you are someone experienced in computer programming/coding, then this idea will definitely be right up your alley. Thing is if you go wrong somewhere, it becomes much more difficult to solve that problem if you’ve done the task in a complex manner. A good tip to keep in mind is to always take the point of view of someone trying to understand how you have solved the problem.

Considering Variance

Another constant aspect of the nature of large amounts of data is that it varies – a lot. The variance within a data set itself and multiple other data sets is so high that it is definitely something worth considering and spending your time analyzing over. The idea behind this is pretty much in line with all of the reasoning logics mentioned for the above advice tips as well.

You want to make your job as easy as possible. This means fleshing out your data in the simplest manner possible so that performing operations on it also becomes easier. The same logic also applies when it comes to variance as well.

If there is too much variability in the data that you are analyzing, you need to study it or if possible, remove it to make things easier. Uncertainty has always been part of statistics and it is not going to change anytime soon. Sometimes, it is simply not possible to get rid of said variance. When that happens, you need to make sure that your reports cover this particular point so that a reference to it can be made later on if and when required. It is an important part of statistics that many overlook but is all the more very important.

Try and Replicate

They say imitation is the purest form of flattery. This line of thinking goes well with statistics too. In most cases, you will be dealing with methodologies and processes involved in performing a certain task well. A lot of the times, you may have to find out the required process on your own by forming your own logic and applying it on the given problem.

However, sometimes there are already existing solutions to some patent problems. This is where your resourcefulness should come into play because it is ideal that you use the given solution to get your job done. Sure, you won’t get the chance to apply yourself on the problem but at the end of the day, if the existing answer is more efficient, why waste time?

Don’t be afraid to ‘borrow’ certain techniques or procedures from someone who has already done it before. If anything, you may find ways of improving upon them and adding to their already existing strengths and efficiency.

This will obviously not be easy and it is up to you to research the topic with everything you’ve got. Once again, understanding is key. What’s more, simply copying the solution is also not the right mentality to go with. Try to understand why something works and perhaps next time you too will be able to come up with something revolutionary.

Make it Reproducible

Yet another thing that newbies seem to forget that a certain problem once solved maybe needed once again in the future. In such a scenario, you do not want to be going through the entire process all over again just to arrive at the same conclusion. It is a waste of time, it is a waste of resources, it is a waste of your energy and it is an overall inefficient way to go about doing things.

What you want to do is the first time you do solve a problem, make it so that it is reproducible in the future. That way you will always have a reference to it if the situation ever arrives. Any future problems depending on the same logic and methodologies can be solved in an instant instead of having to wait all the way.

It’s not just about the process either. The analysis aspect of your work should also follow the same rule. Whatever analysis you do, whatever you observe should all be remarkable so that documentation about it can be made in future scenarios.

Now this may seem a bit vague regarding how exactly you can go about doing this but there are a few ways to generalize:

• Being extremely cautious during all of the steps in observation
• Noting down each and every minute detail about the changes observed
• Sharing the data and code with others so that they too can have their say about it
• Basically be systematic about all of the procedures during each step of analysis

Any veteran in the field of statistics will give you the same tips as well so listen closely and try to absorb all of this up. It will definitely prove to be of much help to you, especially if you are planning to pursue this field as your professional career.