What is sampling and why is it important?
Sampling is a fundamental statistical technique that allows us to perform data analysis efficiently when it is not possible to compute the entire population due to computational capacity limitations. Through sampling, we obtain and analyze a small representative portion of the entire data set. This practice is essential in various fields, such as science and large-scale computational problems, and allows us to generate valid conclusions even without examining the entire data.
There are two main types of probability sampling:
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Random sampling: this approach selects random samples from the population, ensuring that each member has an equal chance of being chosen. It has been used throughout the course mentioned in the transcript and is effective when the population has homogeneous characteristics.
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Stratified sampling: Ideal when there are relevant subgroups within the population. It allows the population to be divided into statistically significant subgroups before random samples are taken from each, ensuring that differences within the population are adequately reflected in the conclusions.
When and how to use stratified sampling?
Stratified sampling is especially useful when within a population there are subgroups with distinct characteristics that could influence the results of the analysis. For example, if we want to assess how many students know how to program within an educational platform such as Platzi, we could be prone to bias if we only drew a random sample from the entire population, since many of the careers are in technical areas.
To avoid this:
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Identify subgroups: First, we segment the population into clearly defined subgroups that reflect significant programmatic differences, such as careers or areas of specialization.
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Random selection of subgroups: Within each subgroup, we select random samples. This method ensures that each subgroup is proportionally represented in the analysis.
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More accurate conclusions: With these steps, the conclusions obtained are more consistent and better reflect the reality of the total population, eliminating biases that could arise from a direct random analysis.
Applications of sampling in daily life
Sampling techniques are not only applicable in academic or financial fields, but also in everyday situations. The transcript suggests practical ways to apply sampling:
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Technology: determine how many mobile devices are Android or how many cameras are of a specific brand.
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Automobiles: Calculate what percentage of cars are specific colors, such as red, or how many are sports cars.
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Architecture: Analyze how many buildings exceed a certain number of stories.
These applications highlight the importance of developing cognitive skills to solve everyday problems efficiently with sampling techniques. By improving these skills, you will not only increase your analytical capabilities, but you will also be able to implement them in your professional and personal environment.
If you have questions, remember that feedback systems are available to answer them. See you in the next class!
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