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When use correctly, statistical data can be used to improve an array of areas from efficiency, to lead time, and profit. But in order to make improvements you need to know how the data has been collected initially. This article is about Statistical data collection methods.There are four main Statistical data collection methods:

• Census
• Sample survey
• Experiment
• Observational study

Each of these methods has it's own set of advantages and drawbacks, that's why one must be aware of all their characteristics to be able to choose the right method according to the individual situation. Here is a brief definition of each method:

• Census - A census is a case study that acquires data from every population member. For the majority of cases, a census is not practical, due to the large amount of time and cost required to conduct it.
• Sample Survey - A sample survey is a case study that obtains data only from a subset of the entire population, not every member, as oppose to Census, so it's much more practical and efficient to carry out, but the results might not be that accurate. For best results using this method it may be appropriate sub-categorize your target group and take a sample set from each sub-category. A basic example would be different ethnic groups.
• Experiment - The experiment is a controlled study in which researchers try to understand the cause-and-effect relationships, how one thing affects another.
• Observational study - Observational studies also try to discover the cause and effect relations, but unlike experiments, they are not able to control how subjects are assigned to groups.

As it was already pointed out, every method has its own pros and cons, so one must be able to know and make a decision regarding which method should be applied in a given situation. There are three factors that should affect this decision and they are - resources, generalizability, causal inference.

If resources are the main factor, then obviously with such a large population, a sample survey has an advantage over census. If the sample survey is well designed, then it can definitely provide results that are really close to the actual figures (high level of accuracy), and it will be done in a quicker and cheaper manner, requiring less man power than a census.

Generalizability stands for applying findings from a sample study to a larger population. Generalizability requires random selection. In case the participants in a study are randomly selected from a larger population, it is appropriate to generalize study results to the larger population, otherwise it might provide accurate results.

Statistical data collection methods are essential for sustainable economics, social and environmental development. We are living in the 'Information Age' where certain data sets are growing in size and complexity, reaching massive proportions, that's why such data collection methods are so important.

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