Data, information and knowledge: starting in Statistics

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Let's start today by talking about data, the basics of Statistics. After all, what is statistics and why should we pay attention to data?

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Course

To equip students in the use of basic statistical techniques for exploratory data analysis. To present problems in the field of logistics that involve the use of statistical data analysis in decision-making.

Statistics books for the student

BUSSAB, Wilton de O.; MORETTIN, Pedro A. Basic Statistics. Saraiva, 2017.

Attendance will be taken! There will be a quiz and an exam!

Introduction

What is statistics?

  • Statistics is a set of methods used to analyze data
  • Statistics can be applied in practically all areas of human knowledge. From observation and inference using data;
  • Its objective is the collection, reduction, analysis, and modeling of data;
  • These are methods that allow questions to be answered scientifically while minimizing the necessary effort as much as possible.

Statistics quickly comes to contribute to the steps of the scientific method. In summary, in this method, the scientist seeks to answer a question and may also test a hypothesis; they collect important information from experiments or observations and draw conclusions from them.

Statistical help comes through methods of data visualization and analysis, extracting the necessary information.

Data

Data... data... data... in the end, isn’t everything data? Let’s analyze the following statement:

“Today it is hot”

- Little guy on the street

Depending on the point of view where you are, "today it is hot" can be information or data. If we categorize days as only hot and cold, hot is data that does not provide any relevant information; after all, it may be 18 degrees or 5 degrees, but we do not know the cutoff that makes a day hot or cold (nor do we know whether we are talking to an Eskimo or someone from Rio de Janeiro). In another situation, where we see a person sweating while saying the phrase above, using prior knowledge, we realize that hot gives the connotation of an environment that the human body cannot tolerate; thus, we have information that would indicate the temperature is probably above 30 degrees.

Therefore, we realize that data do not have relevant meaning and do not lead to any understanding. They represent something that makes no sense at first glance. Therefore, they have no value for supporting conclusions, much less decisions.

Information, on the other hand, would be the ordering and organization of data in such a way that they can be interpreted to convey meaning.

Finally, similar to the scientific method, with information we generate a bit of knowledge about the situation presented.

We can thus build a pyramid, where the farther we move away from data, the more abstraction we have: [1]

With data we can generate information and subsequently knowledge
With data we can generate information and subsequently knowledge

Another example to understand abstraction:

MANGO.

Can we extract any information from the data "mango"? Is mango a fruit or is it a shirt sleeve? To generate information we need the context found or some level of abstraction!

If we bring this abstraction to other fields, we realize that all are subject to it. Information being presented correctly, without room for other interpretations, is extremely difficult, and this concern is also part of statistics.

In the problem of "today it is hot" we also perceive another concern of statistics: How do we categorize as hot or cold? What would be the cutoff that makes a day hot or cold? Why did I say "above 30 degrees probably"? Later on, we will see that we do not need to analyze every day to know whether one is hot or not; the use of probability allows us this ease.

We must note that, if there were a world with infinite time and money, statistics would be much simpler. The discussion made here would not be so relevant. If there were doubt in the data, just analyze everything. If you did not know what hot is, go back and look at all the days. The problem is that the current world does not have these characteristics and without statistics, simple questions would be extremely costly.

Work to think about

1) What data do companies like Facebook and Google collect?

2) What information can we extract from them?

Conclusion

Therefore, the usefulness of statistics is:

- To reduce the costs of the scientific method in terms of money and time;

- To convey information as clearly as possible, preventing other interpretations;

- To generate knowledge and conclusions from data analysis (statistical inference);

- To create standardized and scientific methods to answer questions;

References

[1] VALENTIM, Marta Lígia Pomim. Competitive intelligence in organizations: data, information, and knowledge. DataGramaZero, Rio de Janeiro, v. 3, n. 4, p. A02-1001, 2009.

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