To me, as someone who has done sciences to university level, doubting ability to do statistical sampling well is like doubting the earth is round. But I guess it can’t be taken for granted that other people haven’t learnt about it:
Here is Yougov link:
https://yougov.co.uk/about/panel-methodology
Here is the AI overview I got when I put in statistical sampling for dummies from Google:
Statistical sampling is a way to learn about a large group (a population) by studying a smaller, representative part of it (a sample). It's like tasting a soup to see if it needs more salt – you don't need to eat the whole pot to know! In statistics, this involves carefully selecting a sample to make inferences about the entire population.
Why use statistical sampling?
Efficiency:
Studying an entire population can be costly, time-consuming, and sometimes impossible. Sampling allows for quicker and more affordable data collection.
Accuracy:
If done correctly, a sample can accurately represent the population, allowing researchers to draw reliable conclusions.
Feasibility:
For some populations (like all humans on Earth), it's simply not practical to study every individual.
Key Concepts:
Population:
The entire group you're interested in (e.g., all registered voters in a city).
Sample:
A smaller, representative subset of the population.
Sampling methods:
Different ways to select a sample (e.g., random sampling, stratified sampling).
Bias:
A systematic error that makes the sample unrepresentative of the population.
Margin of error:
The amount of error you can expect when using a sample statistic to estimate a population parameter.
Confidence level:
The probability that the sample results fall within a certain range of the true population value.
Sampling distribution:
A distribution of sample statistics (e.g., sample means) from multiple samples of the same population.
Example:
Imagine you want to know the average height of all students in a large university. Instead of measuring every student, you could randomly select a sample of 100 students and measure their heights. If your sampling method is sound, you can use the average height of those 100 students to estimate the average height of all students in the university.
Types of Sampling Methods:
Probability sampling:
Every member of the population has a known chance of being selected. This is generally preferred for making strong statistical inferences.
Simple random sampling: Each individual has an equal chance of being chosen.
Stratified sampling: The population is divided into subgroups (strata), and samples are drawn from each.
Non-probability sampling:
The probability of selecting any individual is not known. This is often used when probability sampling is not feasible, but it comes with a higher risk of bias.
In essence, statistical sampling is a powerful tool that allows us to gain insights about a large population by studying a carefully selected subset. By understanding the principles of statistical sampling, you can critically evaluate research findings and make informed decisions based on data.