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The Difference between a Population and a Sample in Sports Betting

Statistics is an important part of sports betting that punters must master so they will be able to place a smart bet. This article will describe two important types of data sets – samples and population – and the difference between them. This knowledge can help you avoid mistakes that have the potential to cause bankroll misery. We will try to clarify these relevant terms, as well as the terminology related to them.


Population

A statistical population is a set of entities we can use to draw statistical inferences, usually based on a random sample taken from the population. For example, if you want to male generalizations about all cows, your statistical population is represented by all the cows that exist, existed or will exist. Because it is impossible to observe this statistical population due to insufficient geographical accessibility, resources and time constraints, the researcher must observe a statistical sample from that population.

In sports betting, the population is represented by all objects within the group you wish to study. A parameter is any measurable trait of a population, for example the arithmetic mean that can be considered an average value. If you wanted to measure the home and away wins as the draw percentage in a 3 leagues, the population will be the result of the count and the parameters the home win percentage, the away percentage and the draw percentage.

Sample

In research, statistics and sports betting, a data sample is a set of data selected or collected from a statistical population. Because the population is very large, it is impossible to make a complete enumeration of all its values. In order to make inferences or extrapolations, samples are collected and statistics are calculated from them. This process is called sampling. If the data is drawn from the population with replacement it is called a multisubset, and without replacement a subset of population. A sample is used to estimate the parameters of a population. In order to avoid biases, one must collect a random sample, a sample where each member of the population has a non-zero chance of being selected.

We can explain this better if we use the same example presented above. Let’s assume you want to analyze and measure the home and away wins concerning 3 leagues, but you only collect data form the first two. You now have a sample of the population. You can now use statistics such as the percent of away wins to calculate the population parameter (the win percentage in all 3 leagues).

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