This video tutorial explains the concepts of sample and population in statistics. It uses an example of a disease X study to illustrate the difference between a sample and a population. The video discusses the challenges of studying entire populations and the use of random selection to create samples. It also explains the difference between statistics and parameters, and addresses issues like sampling error and selection bias. The tutorial concludes with a call to action for viewers to engage with the content.

What are the two key terms introduced in the video related to statistics?

• Sample and Population

• Probability and Distribution

• Mean and Median

• Variance and Standard Deviation

In the context of the video, what does the population refer to?

• All people using Facebook

• A group of 50 people with disease X

• All people with disease X worldwide

• All people in a city

Why is it challenging to study an entire population?

• Due to lack of interest in studies

• Because populations are too small

• Due to the difficulty in recruiting every individual

• Because populations are always changing

What is a sample in the context of the video?

• A small group selected from the population

• The entire population

• A group of people from the same city

• A group of people with different diseases

Why do scientists use samples instead of entire populations?

• To include more variables

• To save time and resources

• To ensure accuracy

• To avoid bias

What is the term for a measure derived from a sample?

• Variable

• Statistic

• Constant

• Parameter

What symbol is used to represent the mean of a sample?

• Pi (π)

• Sigma (σ)

• Mu (μ)

• X-bar (x̄)

What is a common cause of sampling error?

• Using a small population

• Differences in characteristics between sample and population

• Random selection of a sample

• Using a large sample size

What is selection bias?

• When the sample is too small

• When the sample is not randomly selected

• When the sample is identical to the population

• When the sample is too large

How can selection bias occur in a study?

• By advertising only on specific platforms

• By using a large sample size

• By including all possible participants

• By using random selection