What Are The Types Of Sampling Techniques In Statistics - Random, Stratified, Cluster, Systematic

Summary

The video explains the importance of sampling in research to gather data about populations. It highlights four main sampling methods: random, stratified, cluster, and systematic sampling. Random sampling gives all members an equal chance of being selected, while stratified sampling ensures each subgroup is represented. Cluster sampling targets specific sections of a population, and systematic sampling selects members at regular intervals. It also mentions convenience sampling, which is prone to bias. The video concludes by noting that even with the best techniques, sampling errors can occur.

Takeaways

• 🔍 A sample is a part of a population used by researchers to collect data about variables.
• 🎯 There are four main sampling techniques: random sampling, stratified sampling, cluster sampling, and systematic sampling.
• 🎲 Random sampling gives every member of the population an equal chance of being selected.
• 📊 Stratified sampling involves dividing the population into subgroups and taking a random sample from each subgroup.
• 🏙️ Cluster sampling involves dividing the population into clusters, then selecting one or more clusters and using all members of the selected clusters as the sample.
• ⚠️ Cluster sampling can be cost-effective but may not always represent the population well.
• 🔢 Systematic sampling assigns a number to each population member and selects members at regular intervals starting from a random number.
• 📝 A sampling error can occur, which is the difference between the sample results and the population results.
• 🚶 Convenience sampling involves selecting members of the population that are easy to reach, but it often leads to biased results.
• 📈 Even with the best sampling methods, researchers should be aware of potential errors or biases in their samples.

Q & A

What is the purpose of using a sample in research?

A sample is used to collect data and information about a variable or variables from a larger population. It helps researchers analyze a subset of the population instead of the entire group, which is often impractical.

What are the four main sampling techniques mentioned in the script?

The four main sampling techniques are random sampling, stratified sampling, cluster sampling, and systematic sampling.

How does random sampling ensure unbiased data collection?

Random sampling ensures unbiased data collection by giving every member of the population an equal chance of being selected for the sample.

What is a stratified sample, and when is it used?

A stratified sample involves dividing the population into subgroups based on shared characteristics, and then selecting a random sample from each subgroup. It is used when researchers want to ensure representation from different segments of the population.

What is the main difference between stratified sampling and cluster sampling?

In stratified sampling, the subgroups have similar characteristics, while in cluster sampling, the clusters are intended to vary in characteristics.

How does cluster sampling work, and when is it useful?

Cluster sampling involves dividing the population into sections or clusters, randomly selecting one or more clusters, and using all members of the selected clusters as the sample. It is useful when the population is large or geographically dispersed, making it cost-effective and efficient.

What is an example of systematic sampling?

An example of systematic sampling is selecting a random starting number, such as 234, and then choosing every 20th member from that starting point to create a sample. This method is used when the population can be easily numbered.

What is a sampling error, and why can it occur?

A sampling error is the difference between the results of a sample and the actual population. It can occur even when using the best sampling methods, due to the inherent variability between samples and populations.

What is convenience sampling, and why can it lead to biased results?

Convenience sampling involves selecting a sample from members of the population that are easy to access or convenient. It often leads to biased results because the sample may not be representative of the entire population.

What are some methods researchers can use to create a random sample?

Researchers can create a random sample by numbering each member of the population, drawing numbered cards, using a calculator or computer to generate random numbers, or using a random number table.

Outlines

00:00

📊 Understanding Sampling Methods

This paragraph introduces the concept of sampling in research, where a sample is a subset of a population used to gather data about a variable or variables. To ensure unbiased results, researchers employ four main sampling methods: random sampling, stratified sampling, cluster sampling, and systematic sampling. Each method is designed to accurately reflect the population while overcoming practical challenges of data collection.

🎲 Random Sampling Explained

A random sample gives each member of the population an equal chance of selection. Researchers can achieve this by numbering individuals and using methods such as drawing numbers from a hat, using a calculator or random number generator, or using a random number table. This method ensures fairness and eliminates selection bias.

🔀 Stratified Sampling Overview

Stratified sampling involves dividing the population into subgroups, or strata, based on shared characteristics. Researchers then take a random sample from each subgroup to ensure that all segments of the population are represented. For instance, if a study seeks to explore annual savings habits across age groups, researchers would collect random samples from people in their 20s, 30s, 40s, and 50s, ensuring a balanced representation across ages.

📦 Cluster Sampling Simplified

Cluster sampling divides a large population into clusters, often based on geographical regions or sections. Researchers randomly select one or more clusters and include all members of those clusters in the sample. This technique is particularly useful for studying large or geographically dispersed populations. However, it may lead to biases if the selected clusters don't fully represent the population.

🔑 Key Differences Between Cluster and Stratified Sampling

While both cluster and stratified sampling involve dividing the population, the subgroups in stratified sampling have similar characteristics, whereas clusters in cluster sampling are intended to vary in characteristics. This distinction helps ensure that stratified samples represent different characteristics equally, while cluster samples aim for geographic or sectional convenience.

⏱️ Systematic Sampling Explained

Systematic sampling involves selecting individuals at regular intervals after a random starting point is chosen. For instance, in a study of Netflix viewing habits in an apartment complex, the researcher could randomly select a starting unit number, then choose every 20th unit thereafter until the desired sample size is reached. This method is efficient if the population can be easily numbered.

⚠️ Sampling Errors and Convenience Sampling

Sampling errors, which represent the difference between the results of a sample and the entire population, can still occur even with the best methods. Convenience sampling, which involves selecting participants that are easy to access, often leads to biased results. Despite its ease, this method is less reliable due to the potential for significant bias in representation.

Keywords

💡Sample

A sample is a subset of a population used by researchers to collect data. In the video, it is described as a method to gather information about variables from a larger population. For example, instead of surveying an entire population of small business owners, researchers may select a sample to represent the broader group.

💡Population

A population refers to the entire group that researchers are interested in studying. In the context of the video, it represents the larger pool from which a sample is drawn, such as all business owners in a city or all members of an apartment complex.

💡Random Sampling

Random sampling is a method where every member of the population has an equal chance of being selected. It ensures that the sample is unbiased. In the video, this is explained through an example where numbered cards are randomly selected from a bowl, or a computer generates random numbers.

💡Stratified Sampling

Stratified sampling divides the population into subgroups or strata, and a random sample is drawn from each subgroup. The video explains this by using age groups (e.g., people in their 20s, 30s, 40s) as subgroups to understand savings habits across different age ranges.

💡Cluster Sampling

Cluster sampling involves dividing the population into sections or clusters and randomly selecting one or more clusters. All members of the chosen clusters are included in the sample. The video gives the example of surveying small business owners by dividing them by zip codes and selecting a few clusters for study.

💡Systematic Sampling

Systematic sampling involves selecting members of a population at regular intervals from a randomly selected starting point. The video describes an example where apartments are numbered, and every 20th unit is chosen after the random starting number is picked.

💡Sampling Error

A sampling error is the difference between the results obtained from the sample and the actual results from the entire population. The video notes that even the best sampling methods can result in errors, highlighting the challenge of achieving perfect accuracy.

💡Convenience Sampling

Convenience sampling is when researchers select a sample from members of the population that are easiest to reach. The video mentions that this method often leads to biased results because it does not accurately represent the entire population.

💡Unbiased

Unbiased refers to a sample that is free from favoritism or systematic error. The video emphasizes the importance of using unbiased sampling methods, like random sampling, to ensure that the data collected accurately reflects the larger population.

💡Subgroup

A subgroup is a smaller, distinct group within a larger population that shares certain characteristics. In stratified sampling, subgroups are used to ensure that different segments of the population are adequately represented. For example, age groups are used as subgroups in the video when analyzing people's savings habits.

Highlights

A sample is part of a population, and researchers use samples to collect data and information about variables from the larger population.

To obtain unbiased samples, there are four main sampling techniques: random sampling, stratified sampling, cluster sampling, and systematic sampling.

A random sample gives every member of the population an equal chance of being selected.

One way to conduct random sampling is to number each member of the population and select random numbers, either manually or using a computer.

A stratified sample divides the population into subgroups based on similar characteristics, and a random sample is taken from each subgroup.

Cluster sampling divides the population into clusters, and a random selection of one or more clusters is used to sample all members from those clusters.

Cluster sampling is useful for large populations or wide geographic areas but may sometimes not represent the entire population.

The key difference between stratified and cluster sampling is that stratified sampling involves subgroups with similar characteristics, while cluster sampling involves subgroups with varied characteristics.

Systematic sampling assigns a counting number to the population and selects members for the sample at regular intervals.

An example of systematic sampling is using a random starting number and a consistent interval to select participants.

Systematic sampling is simple and works well if the population can be easily numbered.

Sampling error is the difference between sample results and actual population results, which can still occur even with the best methods.

Convenience sampling involves selecting members of the population that are easy to access but often leads to biased results.

Each sampling method has its strengths and weaknesses, and the choice depends on the population and research context.

Even the best sampling techniques are not foolproof, and researchers must be aware of potential biases and errors.

Transcripts

A sample is part of a population, and researchers  use samples to collect data and information about  

a variable or variables from the larger population.  To obtain samples that are unbiased there are  

mainly four different sampling techniques or  methods, random sampling, stratified sampling,  

cluster sampling, and systematic sampling.  A random sample is a sample where every  

member of the population has an equal chance of  being selected. There are a few different ways  

to do this, the researcher could number each  member of the population, to keep it simple  

say a population of 90 members, he or she could  then place numbered cards 1 through 90 in  

a hat or bowl or mixer, and select as many cards  as needed to complete the sample. Or they can  

use a calculator or computer to generate random  numbers, or they could use a random number table  

like this one. A stratified sample is where  a researcher will divide the population into  

subgroups to have members from each segment of  the population, and a random sample is derived  

from each subgroup. For example let's say you  wanted to know how much money people saved on  

a yearly basis, you could have subgroups of people  in their 20s, in their 30s, in their 40s, and in  

their 50s. You would then take a random sample  for each of these groups. A cluster sample is  

obtained by dividing the population into sections  or clusters, then randomly selecting one or more  

of the clusters and using all of its members, as  members of the sample. This is often used when the  

population is large or there's a large geographic  area. For instance let's say you wanted to survey  

small business owners in a very populated city, it  would be costly and time-consuming to survey every  

single small business owner. So you could create  a cluster sample, using zip codes and maybe survey  

two or three of the thirteen different possible  zip codes. Cluster samples can be efficient and  

cost effective, however there are times when the  cluster does not represent the population. A little  

note, the main difference between cluster sampling  and stratified sampling is that subgroups in the  

stratified sample have similar characteristics  and the subgroups or clusters in the cluster  

sample are intended to vary in characteristics.  A systematic sample is where a researcher will  

assign a counting number to have of the population,  then select a random number, then select members  

for the sample at regular intervals, from the  starting random number that was selected. For  

example let's say you wanted to know how much time  people living in a singles only apartment complex  

spent watching Netflix on a weekly basis. You would  want to get, say, a sample of 50 members. If there  

were a thousand units in the complex you could  number the units one to a thousand and generate  

a random starting number, say 234, since you want a  sample of 50, you could divide 1,000, the number of  

the population, by 50, to get 20, which would be  your interval number. So, 234 would be selected  

for the sample, then 254, which is 234 plus 20, then  274, which is 254 for plus 20, and so on until you  

had your 50 members selected for your sample. This  sampling method is easy to use if the population  

can be easily numbered. Another note, even when  using the best sampling methods a sampling error,  

which is the difference between the results of  a sample and a population can occur, and there  

is one other sampling method called a convenience  sample, where a researcher develops a sample from  

members of the population that are easy to get or  convenient. Many times these samples lead to biased  

results. Alright my friends, I have more videos  right there for you, till next time, I am outta here.