Sampling - what is it?

Sampling is the process by which a subset of the whole population of data is selected, inspected, or analyzed. It is often used as an alternative to measuring the entire population. Sampling can be used when it is logistically impossible or too expensive to measure every item in a population: for example, if there are millions or billions of items (e.g., web pages) and only limited resources available for capturing and analyzing them. Sampling methods may also be used to reduce bias when making inferences about a target population (see the section on selection bias).

Good practice for data collection - is to take a representative sample from the whole population of data.

Data sampling is the process of selecting a subset of the population to study.
It is used to make decisions about populations, or subpopulations, on the basis of observations from that sample.

Sampling can be done in one of two ways: random sampling or non-random sampling.

How much data to collect, feedback loop and accuracy

Sampling is a strategy to obtain information from a population or subgroup. There are many ways to sample, but the most common methods include random sampling, stratified sampling, and cluster sampling.

In general, if you have 10 questions to ask of your data—and you want to make sure that your experiment is accurate—you must collect enough data to answer all of those 10 questions with high confidence. This means that your sample size should be equal to or greater than the number of questions in the survey (N). For example: If you are asking 5 questions and need 95% confidence in each response, then your N should be at least 20 (i.e., 5 x 4).

How often it should be collected, feedback and reliability

he frequency of sampling depends on the purpose of the monitoring or analysis. For example, if you are trying to measure dissolved oxygen levels in a river, this could be done daily or weekly.

However, if your goal is to establish whether there has been any increase in the level of toxic chemicals in an area over time, then an annual sample may be sufficient. It is important that whatever frequency is decided upon meets the needs of your project and does not lead to unnecessary costs or time delays.

Sample size and sample frequency are often confused.

Sample size and sample frequency are often confused. Sample size is the number of units in a sample; it is dependent on the purpose of the monitoring or analysis. Sample frequency is how often you collect samples; it depends on the question being asked of your data.

The sample size is the number of units in a sample.
It is important to know the sample size for each subgroup (sample) size. The following table provides examples of some common monitoring levels and their sample sizes:

Monitoring level Sample Size

1 0-10% of the population

2 10-20% of the population

3 20-30% of the population

4 30-40% of the population

5 40-50% of the population

A sample frequency is how often you collect samples.

The frequency of sampling is the number of samples you collect in a given time period. The frequency of sampling should be chosen based on the purpose of the monitoring or analysis, as well as the type of data being collected. For example, if you are monitoring pH levels in an aquarium and need to analyze daily changes over an entire month, it would be best to sample once per day because there will be very little change in pH levels between sampling times and this will give you more consistent results than if you sampled every few hours or once per week. On the other hand, if your purpose was simply to determine whether there had been any sudden changes which could potentially harm your fish (such as chlorine accidentally being added), then it would make sense to sample more frequently so that these small changes could be detected quickly and acted upon immediately before they caused damage.

When to choose a subgroup (sample) size depends on the question being asked of the data, and on the purpose of the monitoring or analysis.

When to choose a subgroup (sample) size depends on the question being asked of the data, and on the purpose of the monitoring or analysis.

Sampling is used in a wide range of contexts, so it's important to understand that sampling has different meanings depending on whether:

You're talking about sampling for research purposes vs. sampling for monitoring and analytics purposes.

You're talking about sampling from a population vs. sample from a subset of data points from your dataset.