Understanding Orphaned Workspaces in Power BI and How to recover them

Statistical Sampling 

(Choosing from Left)

Samples are parts of a population. For example, you might have a list of information on 100 people out of 10,000 people. You can use that list to make some assumptions about the entire population’s behavior. Unfortunately, it’s not quite that simple.

This Blog-post will cover

1.Reasons for sampling

2.Sampling methods

3.Advantages and Disadvantages of sampling methods

1.Reasons for sampling.

In sampling, we assume that samples are drawn from the population and sample means and population means are equal. A population can be defined as a whole that includes all items and characteristics of the research taken into study. However, gathering all this information is time consuming and costly. We therefore make inferences about the population with the help of samples.

• Resources (time, money) and workload 
• Gives results with known accuracy that can be calculated mathematically

2.Sampling methods.

When you do stats, your sample size must be optimal  not too large or too small. Then once you’ve decided on a sample size you must use a sound technique for actually drawing the sample from the population. There are two main areas:

1. Probability Sampling
2. Non-probability sampling

1.Probability Sampling

Probability sampling is based on the fact that every member of a population has a known and equal chance of being selected.

For example, if you had a population of 100 people, each person would have odds of 1 out of 100 of being chosen. Probability sampling gives you the best chance to create a sample that is truly representative of the population.

1.1Types of Probability Sampling

• Simple random sampling is a completely random method of selecting subjects. These can include assigning numbers to all subjects and then using a random number generator to choose random numbers. Classic ball and urn experiments are another example of this process (assuming the balls are sufficiently mixed). The members whose numbers are chosen are included in the sample.
• Stratified Random Sampling involves splitting subjects into mutually exclusive groups and then using simple random sampling to choose members from groups.
• Systematic Sampling means that you choose every “nth” participant from a complete list. For example, you could choose every 10th person listed.
• Cluster Random Sampling is a way to randomly select participants from a list that is too large for simple random sampling. For example, if you wanted to choose 1000 participants from the entire population of the U.S., it is likely impossible to get a complete list of everyone. Instead, the researcher randomly selects areas (i.e. cities or counties) and randomly selects from within those boundaries.
• Multi-Stage Random sampling uses a combination of techniques.

Advantages
Cluster sampling: convenience and ease of use.
Simple random sampling: creates samples that are highly representative of the population.
Stratified random sampling: creates strata or layers that are highly representative of strata or layers in the population.
Systematic sampling: creates samples that are highly representative of the population, without the need for a random number generator.
Disadvantages
Cluster sampling: might not work well if unit members are not homogeneous (i.e. if they are different from each other).
Simple random sampling: tedious and time consuming, especially when creating larger samples.
Stratified random sampling: tedious and time consuming, especially when creating larger samples.
Systematic sampling: not as random as simple random sampling,

2.Non-probability sampling 

Non-probability sampling is a sampling technique where the odds of any member being selected for a sample cannot be calculated. It’s the opposite of probability sampling, where you can calculate the odds. In addition, probability sampling involves random selection, while non-probability sampling does not–it relies on the subjective judgement of the researcher.

2.1Types of Non-Probability Sampling

• Convenience Sampling: as the name suggests, this involves collecting a sample from somewhere convenient to you: the mall, your local school, your church. Sometimes called accidental sampling, opportunity sampling or grab sampling.
• Haphazard Sampling: where a researcher chooses items haphazardly, trying to simulate randomness. However, the result may not be random at all and is often tainted by selection bias.
• Purposive Sampling: where the researcher chooses a sample based on their knowledge about the population and the study itself. The study participants are chosen based on the study’s purpose. There are several types of purposive sampling. For a full list, advantages and disadvantages of the method, see the article: Purposive Sampling.
• Expert Sampling: in this method, the researcher draws the sample from a list of experts in the field.
• Heterogeneity Sampling / Diversity Sampling: a type of sampling where you deliberately choose members so that all views are represented. However, those views may or may not be represented proportionally.
• Modal Instance Sampling: The most “typical” members are chosen from a set.
• Quota Sampling: where the groups (i.e. men and women) in the sample are proportional to the groups in the population.
• Snowball Sampling: where research participants recruit other members for the study. This method is particularly useful when participants might be hard to find. For example, a study on working prostitutes or current heroin users.

Advantages

A major advantage with non-probability sampling is that  compared to probability sampling  it’s very cost- and time-effective. It’s also easy to use and can also be used when it’s impossible to conduct probability sampling (e.g. when you have a very small population to work with).
Disadvantages
One major disadvantage of non-probability sampling is that it’s impossible to know how well you are representing the population. Plus, you can’t calculate confidence intervals and margins of error. This is the major reason why, if at all possible, you should consider probability sampling methods first.