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Introduction to statistics


Statistic Object

Statistics is an exact science that aims to provide the analyst with insights to collect, organize, summarize, analyze and present data. It deals with parameters extracted from the population, such as mean or standard deviation.

Statistics provides us with techniques for extracting information from data, which is often incomplete as it gives us useful information about the problem under study, so it is the objective of Statistics to extract information from data to gain a better understanding. of the situations they represent.

When addressing a problem involving statistical methods, these should be used even before taking the sample, that is, planning the experience that will allow us to collect the data, so that later we can extract as much as possible. information relevant to the problem under study, ie to the population from which the data comes.

When in possession of the data, we try to group and reduce them, in the form of a sample, leaving aside the present randomness.

Then the objective of the statistical study may be to estimate a quantity or test a hypothesis, using convenient statistical techniques, which enhance the full potential of statistics, as they will allow conclusions to be drawn about a population, based on in a small sample, still giving us a measure of the mistake made.

Click here to see example 1

Population and sample

Any scientific study faces the dilemma of population or sample study. Obviously, a much higher accuracy would be obtained if the whole group, the population, were analyzed than a small representative portion, called the sample. It is observed that it is impracticable in most cases to study the population due to distances, cost, time, logistics, among other reasons.

The alternative practiced in these cases is to work with a reliable sample. If the sample is reliable and provides inferences about the population, we call it statistical inference. For the inference to be valid, good sampling is required, free from errors such as lack of correct population determination, lack of randomness and error in sample sizing.

When it is not possible to study exhaustively all the elements of the population, we study only a few elements, which we call the Sample.

Click here to see example 2

When the sample does not represent correctly the population is biased and its use may give rise to misinterpretations.

Next: Census, Descriptive and Inductive Statistics