Sampling: Population vs. Sample, Random Sampling, Stratified Sampling

Summary

The video script delves into the concept of sampling as a method for researchers to gather data from a population. It explains the difference between a population and a sample, emphasizing the impracticality of surveying every individual within a large population, such as all Americans. The script introduces two primary forms of sampling: non-probability and probability sampling. It focuses on probability sampling, which includes random sampling and stratified sampling, as a more reliable method for ensuring that every member of the population has an equal chance of being included in the study. The video also discusses the importance of a representative sample for drawing accurate inferences about the population. It concludes by illustrating how these sampling methods could be applied to a hypothetical study on the support for marijuana legalization among Americans, suggesting the use of stratified sampling to account for the country's diversity.

Takeaways

• 🌟 **Population vs Sample**: The population is the entire set of subjects you wish to study, while a sample is a part or subset of that population.
• 🔍 **Representative Sample**: A good sample should be representative of the population in terms of various characteristics like age, gender, and income to allow for accurate inferences.
• ⚖️ **Sampling Methods**: There are two main forms of sampling: non-probability sampling (based on convenience or volunteer basis) and probability sampling (where every member has an equal chance to be included).
• 🎰 **Random Sampling**: Involves random selection from the population, ensuring every member has an equal chance to be included in the sample.
• 📦 **Stratified Sampling**: Divides the population into strata (subgroups) based on certain characteristics, then randomly selects from each stratum to ensure representation of each subgroup.
• 📈 **External Validity**: A representative sample leads to high external validity, meaning the findings can be generalized to the larger population.
• 🤔 **Sample Size Importance**: The size of the sample matters, depending on the size and variability of the population being studied.
• 📊 **Zip Codes for Random Sampling**: As an example, using zip codes can simplify random sampling by reducing a large population to a more manageable number of units.
• 🧩 **Stratified Sampling for Variability**: Especially useful for large and varied populations, ensuring that each subgroup is represented in the sample.
• ❌ **Sampling Bias**: A non-representative sample can lead to sampling bias, resulting in low external validity and potential errors in study conclusions.
• ✅ **Generalization to Population**: The goal of sampling is to make inferences about the population based on the sample, which is more feasible and cost-effective than studying the entire population.
• 📝 **Practical Application**: The concepts of sampling can be applied to real-world research questions, such as determining the percentage of Americans supporting the legalization of marijuana.

Q & A

What is the primary challenge in asking every American about their support for the legalization of marijuana?

The primary challenge is that it would be too time-consuming and expensive. The United States has over 330 million people, making it nearly impossible to reach every individual.

What is the difference between a population and a sample?

A population represents the entire set of something that you wish to study, which could be people, objects, or a specific subgroup. A sample is a part or a subset of the population, representing a smaller percentage of the total.

Why is sampling used in research?

Sampling is used because it is often impractical or impossible to study an entire population. By taking a representative sample, researchers can make inferences about the population based on the sample's characteristics.

What are the two main forms of sampling?

The two main forms of sampling are non-probability sampling and probability sampling. Non-probability sampling is often based on convenience or volunteer participation, while probability sampling involves random selection, giving every member of the population an equal chance to be included.

How does random sampling ensure that every member of the population has an equal chance to be included in the study?

Random sampling uses a process akin to picking names out of a hat, where individuals are selected randomly from the population. This can be done using a computer or an Excel sheet that randomizes the selection process.

What is the importance of a sample being representative of the population?

A representative sample ensures that all characteristics and features of the population, such as different ages, genders, incomes, and backgrounds, are reflected in the sample. This allows for better inferences and conclusions to be drawn about the population from the sample.

What is the concept of external validity in research?

External validity refers to the ability to generalize the findings of a study to the real world or the general population. A study with high external validity can confidently apply its results to the population at large.

What is sampling bias and how can it affect a study?

Sampling bias occurs when the sample is not representative of the population. This can lead to low external validity, meaning the results cannot be generalized to the population, potentially leading to errors in the study.

How does the size of the population and its variation influence the sample size needed for a study?

The size of the population and the amount of variation within it affect the sample size needed. Larger and more varied populations typically require a larger sample size to ensure that the sample is representative and that the study has high external validity.

How can zip codes be used in random sampling to study a large population like the United States?

Zip codes can be used to randomly select areas for study instead of individuals. By randomizing a list of zip codes, researchers can contact people within those selected areas, making the process more manageable and representative.

What is stratified sampling and when is it preferred over random sampling?

Stratified sampling involves dividing the population into subgroups, or strata, based on specific characteristics and then randomly selecting from each stratum. It is preferred over random sampling when the population has a lot of variability to ensure that every subgroup is represented in the sample.

How can stratified sampling be applied to determine the percentage of Americans who support the legalization of marijuana?

Stratified sampling can be applied by dividing the population into strata based on factors like gender, race, income, and educational background. Then, a random selection is made from each stratum to ensure that the sample is representative of the diverse population, allowing for more accurate inferences about the population's views on marijuana legalization.