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Sana Shafaqat Official

Tech’s Impact on Statistics: Is It Enhancing or Undermining Data Analysis?

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For centuries, statisticians have tried to understand the world around them. They have used math and logic to collect data from multiple sources, analyze that data, and then use it to attempt to understand how things work. As technology has improved over time, we have also become more able to collect data from different sources around the globe due to the advancement of technology. It’s no secret that computers can make mistakes, but it’s just that we are only sometimes aware of them when they do or when they don’t make them.

Humanity's ability to collect and compute data has dramatically increased our understanding of the world.

Statistics is a way to understand data. It’s how we get better at understanding the world and how we can help solve problems.

 

Data collection and computation have greatly improved our understanding of the world, but it is possible to raise expectations by going too far with this technology. 

We don’t want to start utilizing statistics as a means to an end. In other words, when we want to make everything into a statistical study, the results favor those with more resources and time than others.

 

The problem with statistical analysis can demonstrate a wide range of outcomes, particularly if incorrect statistical methods are applied or people make erroneous assumptions, which can cause a wide range of results to appear. 

 

If you want to know what we mean by the term “statistically significant,” you should always ask yourself what it means to us. How do we determine how much weight to give to a particular result based on the results it yields? Can this result be achieved solely by chance, and how likely is it for us to achieve it?

 

When we say “statistically significant,” what do we mean? The most common approach is to compare a result against the null hypothesis. When you examine a data set, you can arrange it in many possible ways. 

 

For example, suppose you had 100 people in your sample and asked them whether they preferred chocolate ice cream or vanilla ice cream. In that case, some might choose one over the other (say, 50% of people like chocolate), while others would have no preference (25%).

Today, there are so many data sources worldwide that no individual can understand them all.

As we live in a data-driven world, there are so many data sources available worldwide that it is only possible for some individuals to be proficient in all of them. Typically, the amount of data generated exponentially and at a rate that exceeds our ability to absorb it is also growing exponentially.

 

In addition, this increase in available information has led to an increasing number of people who can begin to understand these many sources (and even more who claim they do). But when you put all these facts together, The number of people with access to all kinds of information needs to grow more quickly.

 

Considering the variety of data sources, it would only be possible for some to understand them. An exponential increase in data is outstripping our capacity to absorb it at a rapid pace. Many available sources of information need to catch up with the increase in individuals seeking to grasp these sources, with many of them merely asserting that they are aware of them.

The tendency for statisticians to over-rely on their computers can sometimes be seen. It is essential to remember that computers can also make errors occasionally.

The key to understanding the limitations of your data and how to analyze it effectively is to be aware of its limitations. In the end, you can’t just rely on a computer to do the work for you, even if it seems logical from the point of view of algorithmic reasoning. Computers can make mistakes, too!

 

A vital statistician should be able to explain their results clearly and concisely to other professionals in the field – whether that is another statistician or a non-statistician interested in the topic (or both! ).

 

One of the most important things you’ll learn in a statistics class is how to clearly and concisely explain your results. In many fields, it’s not enough to report your findings—you need to be able to explain them. 

 

In particular, if you are presenting your results to someone other than a statistician, you should know this. If you’re working with a researcher unfamiliar with statistics, ensure they understand what you’re doing and why it’s essential. Try to explain things in simple terms whenever possible since statistics can seem confusing to people unfamiliar with the field.

A good statistician will use technology at their disposal, but they also need to understand what they are doing to avoid mistakes when they analyze data.

You should be aware as a statistician of how your tools’ limitations can impact your analysis and how that can influence your decisions. In addition to having a good understanding of your limitations, you should also be able to explain why specific results are valid or invalid, according to your level of expertise. Finally, you must be able to make sense of what you’ve found for others (including clients) to fully understand what it means for them.

 

Researchers often ask a statistician to explain their findings. This job cannot be accessible when a statistician deals with complex data requiring specialized knowledge and understanding. However, you must be able to communicate your findings in ways that make sense to your audience. If you can do this, then you may be able to get funding for future projects or convince others of the validity of your work.

 

There is no doubt that to excel as a statistician, you need to recognize your tools’ limitations and their impact on the analyses you conduct. Additionally, grasp the boundaries of your knowledge, capable of elucidating the validity or flaws in outcomes. Ultimately, your ability to clarify findings, ensuring clarity for all, including clients, is paramount.

Conclusion

As we’ve seen, there are many ways that technology can make statistics better or worse. Statisticians need to know how their tools work and what potential pitfalls they might encounter when using them. There is no doubt that a good statistician will also make use of the technology available to them. Still, they also need to recognize when it’s time to step back and look at things from an outsider’s perspective—because, after all, the world is more significant than just numbers! 

To conclude, I’d like to give you a few suggestions:

  • Continue to learn about various new tools and methods as they become available.
  • Do not let your career path be defined solely by the skills required today.
  • Always keep an open mind about where else data could take us.

 

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Picture of Sana Shafaqat

Sana Shafaqat

I am a professional statistician and data analyst. I have worked in the field for over five years and have experience with various statistical software packages. I am passionate about data analysis and interpretation and love finding new ways to visualize data. I enjoy reading, spending time with my family, and playing tennis in my spare time.

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