Explain each sampling technique discussed in the “Visual Learner: Statistics” in your own words, and give examples of when each technique would be appropriate.

ADDITIONAL DETAILS 

Sampling techniques discussed in the “Visual Learner: Statistics

Introduction

Sample is used to refer to a subset of a population. The term comes from the Latin word “sampio,” meaning “a small amount.” It was first used by statisticians in the late 1800s when they began analyzing data collected from actual samples rather than entire populations. Sampling techniques are used in many fields, including marketing, social science research and education. In this article we will discuss some key concepts related to sampling techniques:

Sample versus population

The sample is a subset of the population. The entire group you are studying, the population, doesn’t include every single person in it—it’s just that you don’t know who they are. In order to study this group, we need to collect information about some subset of this larger group that we want to look at in more detail and make inferences about their characteristics and behavior.

The sample size is how many people make up your sample; this number might not be exact but will be close enough for our purposes here (which is why we can talk about it). And if we were doing an experiment where there were only five people who were chosen from a large population (like 50), then those five would be our “sample” since they represent all members of this large population!

Descriptive statistics

Descriptive statistics are used to describe the data. The mean, median and mode are all measures of central tendency and they reveal how much the data is spread out over a wide range. The shape of your data set can tell you if it’s symmetrical or not, which means that there are equal numbers of high values as low ones. For example if you have a graph with one spike that represents 70% of your total points (or just under), then this means that most points fall between 60%-70% so it’s not very symmetrical in nature at all!

Inferential statistics

Inferential statistics are used to make inferences about the population from sample data. Inference is the process of making educated guesses or predictions based on your observations. The most common types of inference are hypothesis testing and statistical inference, which can be done using either descriptive or inferential statistics.

Descriptive Statistics – Descriptive statistics measure central tendency, variability and association among variables in a population. It gives us information about how variables behave in a set of cases or observations (e.g., mean) without any assumptions made about what those cases represent (e.g., a single person).

Inferential Statistics – Inferential statistics use probability theory to determine whether something is true based on observation data; if it’s not true then we can reject our hypothesis/theory and go back to collecting new empirical evidence until we find one that fits our model better than others do

The two main types of sampling are probability sampling and nonprobability sampling.

Probability sampling is the most accurate way to gather data. When you use probability samples, you are going to have a better idea of how your results will be distributed. The size of your sample will also be much more accurate and precise than with nonprobability samples.

Nonprobability sampling is less accurate because it doesn’t use random selection; instead, it uses personal judgment or intuition from an expert on what seems like an appropriate group size for each item being measured (e.g., 100 people).

Simple random sample (SRS)

The simple random sample (SRS) is the best way to select a sample from a population. It’s not just the only way, but it’s also unbiased and valid. In other words: SRS is the best option for getting valid results when sampling from a population.

Stratified random sample

Stratified random sampling is a form of probability sampling in which the researcher divides the population into groups with known sizes and then chooses a portion of that group to represent each stratum. For example, if you are studying people who live on farms, you might want to divide up your sample according to age, gender and location (e.g., urban vs rural). You can then randomly select one member from each group for your study sample.

This method works well when you need to ensure that your sample is representative of all possible values within an entire population: for example, if you want an accurate picture of how many cows there are on farms across America! In this case, stratified random sampling would be ideal because it allows researchers who don’t know how many cows there are yet but still want some idea about how big those populations might be

Cluster sample

Cluster sampling is a type of non-probability sampling in which you choose a subset of your population to sample. Cluster sampling is useful for studying large groups that cannot be easily interviewed due to the practical difficulties involved in contacting them all, such as when studying people who live in remote areas or with limited access to transportation.

To use cluster sampling, you must first identify the groups or clusters within your population that would benefit most from your research study. For example, if you want to find out what types of objects are most frequently stolen from households at large shopping centers, it would be best if your sample included all store owners and managers rather than just one person from each store whose job it was keep track of lost items (a tough task).

Nonprobability samples

Nonprobability samples are generally used when you don’t have a lot of data to work with. For example, if you want to study the effects of light on plants, but only have access to a few plants at your local botanical garden, it would be difficult to conduct an experiment with them because there are too few samples for statistical significance. In this case, you could use nonprobability sampling techniques like convenience sampling or snowball sampling:

• Convenience sampling involves asking people who walk into your office if they’d like their picture taken and then taking pictures from those who agree (or perhaps even those who don’t). This method has been used by scientists for decades because it’s cheap and easy; however, this method does not take into account potential bias in questionnaires or other errors that may arise during the process of gathering data from participants.* Snowball sampling involves approaching everyone within earshot—at least one person per household—and asking them if they would take part in research studies.* Both methods can lead researchers down paths where bias is introduced due to social desirability; moreover, neither method accounts for attrition over time which means that even though we may have initially gotten good responses from some participants early on during our study period (e.,g., through convenience), subsequent rounds of recruitment may fail due

Convenience sample

The most common type of sample is known as convenience sampling. This means that you simply choose some people to represent your population and then interview them. If there are many people in your community who could be interviewed, then this may be the best option for you because it’s easy, quick, and inexpensive.

However, convenience samples are not always representative of all groups within society because they don’t take into account possible differences between groups (such as race or gender) or variables such as education level or income level. For example: A researcher might want to study how Americans think about democracy by asking 10 different questions about their views on government; however if he only interviewed white males from upper-middle class backgrounds who had attended college then his research wouldn’t accurately reflect what Americans really think about democracy today!

Judgmental or purposive sample

A judgmental or purposive sample is a type of nonprobability sample.

Unlike random sampling, purposeful sampling is used when you have a specific population in mind and want to study it. For example, if you want to study the effects of exercise on high school students’ academic performance and health habits, then you would use purposeful sampling because this information can be used as evidence for future policy decisions on how best to improve educational outcomes among younger students (who tend toward poorer health).

There are many ways to take a sample.

There are many ways to take a sample. The most important thing is to choose a sample that is representative of the population. In other words, you want your sample to represent all possible combinations of answers from a larger group.

Probability samples are more likely than nonprobability samples because they use random chance (by drawing names) and eliminate bias in choosing who gets included in your survey. Non-probability sampling can result in small groups of people who do not accurately reflect their larger population or be more costly because it requires more resources for interviewing respondents and mailings (or other means).

Cluster sampling is useful when there are many units within an area where you want information about how each unit responds; however, it can be difficult if scattered geographically so try using judgmental or purposive methods instead!

Conclusion

We hope that you have gained a better understanding of the different types of sampling and their uses. As you’re reading this book, remember that every time you decide on how to sample data, there are multiple ways to do so. Even though we discussed only a few in this article, there are many more possibilities out there! And when it comes down to it, deciding which method is best will depend on your specific needs and goals for your study. There isn’t one right answer