Will any question ever really be answered?

Do you need to improve your data literacy? See if you can answer these 5 questions

Have you ever sat in a meeting where someone presented the results of a data analysis that you didn't fully understand, but then didn't ask because everyone else seemed to have it? If this sounds familiar, take some time to improve your data literacy.

Data literacy is the ability to derive meaningful information from data. Fortunately, even if you need to have at least a basic understanding of math and statistics, there are many excellent tools and resources to fall back on. When using data as a basis for making decisions, it is important to understand visualizations, the terminology, and the language of statistics.

These can be simple questions such as the meaning of “dispersion”, but also complex issues such as the use of a decision tree to develop measures. And of course this assumes that the analyzes are based on accurate data. Data literacy doesn't mean you know how to analyze data. Rather, it is about understanding the analyzes that your colleagues or external consultants present to you.

Why is data literacy important?

Organizations now know that when they effectively integrate technology and analytics into their business processes, they can be more profitable, efficient, and customer experience better.

Gartner estimates that by 2023 “data literacy will be an expressly required and necessary driver of corporate value.” The company even believes that data literacy will formally be incorporated into more than 80% of data and analytics strategies and will change management programs. If you have a knowledge of Lean or Six Sigma, you have probably been teaching your coworkers data literacy for years.

Executives know they need to make better use of their data to stay competitive, and many already have plans for creating an "analytics culture". As technology works its way into all areas of business life and more data is available than ever before, the ability to understand basic concepts of data analysis has become a necessary prerequisite for much of what we do.

Improving your data literacy might turn out to be the best thing you can do right now. How many of the following questions can you answer on the fly?

Think for a moment, memorize or write down the answer, and then click or tap the question to find out if you were correct.

With quantitatively are numbers and facts that can be measured objectively (e.g. width, height, temperature or volume). Qualitatively refers to characteristics that cannot be easily measured and are subjectively observed (e.g. smells, tastes, texture or color).

additional Information


The word “average” is often used to refer to the mean - that is, the sum of all numbers divided by the number of numbers added up. The median, on the other hand, is the number that is in the middle.

However, in many studies, including Six Sigma projects, there is data in which the mean is not necessarily the best measure of the mean. Take five items of household income as an example. The first is € 140,000. The second € 200,000. The third € 215,000. The fourth € 220,000. And the fifth € 1,725,000. The mean of these numbers is € 500,000.

However, when we speak of the average, we are actually referring to the number that best characterizes that specific sample. $ 500,000 is much higher than all but one of the numbers, and the median of $ 215,000 is a better "average".

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For example, suppose a scatter plot of ice cream sales and skateboarding accidents has a straight line and a correlation coefficient of 0.9999. But it is very clear that buying ice cream does not lead to skateboarding accidents. More people are skateboarding and buying ice cream when the weather is warm - and that's why these two factors are correlated. Only properly controlled experiments can determine whether there is a causal relationship.

More information (and a fun comic that sums it up nicely!)


In a Observational study nothing is done to the things or people under investigation. They are just being watched as they are. In one controlled experiment they are assigned to groups. Every group (with the exception of the Control group) receives a treatment or is modified in some way (e.g. products are made with an alternative process step or subjects are asked not to take caffeine, etc.) and then the effect of this change is examined.

Not finished the test yet? The Khan Academy has even more background knowledge and scenarios to test yourself and see if you answered this question correctly.

0–2: Don't worry. Data literacy is a journey you've just started!

And you've already got to the Minitab blog. So you've already taken the first step. Stick with it, and Apply to us if you have any questions or want to learn more about solutions analytics other data-driven decision-making if you have any questions or want to learn more about solution analytics and data-driven decision making.

3–4: You have to be reckoned with!

And we could give you even more knowledge with which you can really make an impression. At least when it comes to solving problems through statistical analysis. We encourage you to check out our upcoming Webinar Recordings to look at. To invest even more in your analytical skills, enroll in our guided training courses with experts in Minitab and statistical analysis View the training calendar

5: perfect! Uh How embarrassing ... maybe you work for us?

Or would you like to do that? Here are our vacancies!

If not, we understand, of course ... We recommend you to go through our course on the subject anyway Machine learning for predictive analytics ponder. . Because even if you are a statistician or data scientist and have been able to answer all the questions, you know that there are always new methods and approaches that you could adopt!

 

Final note: Organizations know they need to make better use of their data

When data scientists retreat behind closed doors, there is no easy path to operational excellence or business success. Specialized data analysts may stand by to support individual colleagues with complex problems with their data, but only when data analysis is generally understood is everyone helpful.

Data literacy throughout the company ensures that the data is also included in day-to-day decision-making. It leads to better questions, deeper understanding, and robust conclusions. Improving your data analysis skills will also help you develop personally. When you make data-driven decisions, you eliminate bias and personal opinions that you would otherwise bring into discussions.