Statistics Sampling and Bias Overview

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

What characterizes a good sample?

It is selected randomly without consideration of population traits.

It focuses solely on a specific subgroup, ignoring the larger population.

It represents the entire population accurately in terms of key characteristics. (correct)

It includes a wide variety of elements with no regard to population representation.

Which scenario illustrates a non-response bias?

A survey is distributed, but only 15 out of 50 recipients return it. (correct)

A study surveys a balanced number of participants from each age group.

A researcher randomly selects 100 people and interviews them all.

A sample is obtained by selecting every fifth person from a list.

In which situation would selection bias most likely occur?

Randomly distributing questionnaires to every household in a neighborhood.

Conducting interviews at a public event and everyone attending agrees to participate.

Selecting participants from a comprehensive list of all residents in a city.

Using only volunteers for a health survey. (correct)

What defines a stratified random sample?

A sample taken from distinct subgroups, each sampled randomly. Signup and view all the answers

What is a major downside of using a biased sampling frame?

It can lead to systematically unrepresentative samples. Signup and view all the answers

What is a simple random sample (SRS)?

A sample selected where every element can potentially be chosen equally. Signup and view all the answers

What type of bias occurs when survey participants provide inaccurate responses favoring a specific outcome?

Response bias Signup and view all the answers

Which is a characteristic of a cluster random sample?

Sampling is conducted from entire groups after they are randomly selected. Signup and view all the answers

Which of the following is NOT a source of bias when conducting surveys?

Random sampling Signup and view all the answers

Why can a survey sampling method result in systematic underrepresentation?

It failed to account for diverse population segments. Signup and view all the answers

What defines a sampling frame?

The set of elements from which the sample is taken. Signup and view all the answers

Which scenario illustrates a sampling frame that is not identical to the population of interest?

Choosing names from a telephone directory. Signup and view all the answers

What is the likely effect of bias in a sampling process?

It can systematically affect the estimation of the parameter of interest. Signup and view all the answers

In what way can a sampling frame be considered representative?

When it closely resembles the characteristics of the population. Signup and view all the answers

Which type of sampling design can potentially reduce bias?

Systematic sampling. Signup and view all the answers

What distinguishes bias from random/sampling error?

Bias leads to consistent inaccuracies, whereas sampling error is due to chance. Signup and view all the answers

When obtaining a sample from a street corner, what is a potential source of bias?

The location specifically chosen for sampling. Signup and view all the answers

Which option best describes the ideal sampling frame?

It should perfectly match the population of interest. Signup and view all the answers

When is it acceptable to use a non-representative sampling frame?

When conducting exploratory research. Signup and view all the answers

Which type of sampling is characterized by taking a simple random sample from each segment after partitioning the sampling frame into strata?

Stratified sampling Signup and view all the answers

What is the main advantage of using a cluster sample compared to other sampling methods?

It is the least expensive sampling method. Signup and view all the answers

When is it most appropriate to use systematic random sampling?

When elements can be arranged in sequence Signup and view all the answers

What kind of sample consists of individuals that are easiest to reach for data collection?

Convenience sample Signup and view all the answers

How can sampling error be minimized according to the principles of sampling?

By increasing the sample size Signup and view all the answers

What is a systematic selected sample where every $k^{th}$ member is chosen called?

Systematic random sample Signup and view all the answers

Which sampling design is the most costly but provides insight into different strata of interest?

Stratified random sampling Signup and view all the answers

What type of sampling could lead to larger error if the clusters chosen do not reflect the population's diversity?

Cluster sampling Signup and view all the answers

In what scenario is it inappropriate to use systematic random sampling?

When the sampling frame has cyclical characteristics Signup and view all the answers

Which type of sampling method is often considered non-scientific and results cannot be generalized?

Judgment sampling Signup and view all the answers

Study Notes

Sampling and Data Collection

A sample is a subset of elements from a larger population, used for analysis.
The sampling frame represents the set of elements from which the sample is drawn and should ideally align with the population of interest.

Identifying Components of Data Collection

Identify the sampling frame, sample, parameter of interest, and sampling design from given descriptions.
Examples demonstrate gaps between the sampling frame and population:
- Using a telephone directory does not include all Vancouver residents (unlisted, homeless).
- Approaching pedestrians at a location results in a sample that might include non-residents.
- A registrar's list of students can provide an accurate sampling frame when it matches the population.

Good vs. Bad Samples

A good sample is representative of the population in terms of characteristics relevant to the study.
A bad sample is systematically biased and misrepresents segments of the population.
- Example of bias: surveying friends to estimate food spending results in underrepresentation of other groups like workers or parents.

Types of Bias in Sampling

Selection Bias occurs when the sample is not representative, often due to an improper sampling frame or non-random designs.
Non-response Bias happens if non-responders differ from responders regarding the characteristic of interest, such as GPA in surveys.
Response Bias arises when inaccurate responses skew results, favoring one outcome over another.

Sampling Design

Random Sampling ensures every element has an equal chance of being selected. Types include:
- Simple Random Sample (SRS): Each subset has an equal chance (e.g., lottery).
- Stratified Random Sample: Frame divided into strata, samples taken from each (e.g., different types of clients).
- Cluster Random Sample: Entire clusters randomly selected, then sampled (e.g., schools within a district).
- Systematic Random Sample: Selecting every kth element from a list after a random start.

Sampling Error vs. Bias

Sampling Error occurs regardless of bias; it is the variation between the sample statistic and the population parameter and generally decreases with larger samples.
Bias cannot be remedied by increasing sample size and leads to systematic overestimation or underestimation.

Combination of Designs

Often, a mix of sampling techniques is employed for better representation and efficiency.
Example: Using systematic sampling from different strata of clients in an accounting firm.

Non-Random Sampling

Non-random samples (judgment or convenience samples) can be employed when random sampling is not feasible, though results may not be generalizable.
Judgment samples are selected based on the researcher's discretion, while convenience samples gather readily available data.

Effective Sampling Strategies

Stratified samples are costly but effective when strata have significant differences.
Cluster samples are budget-friendly but can introduce greater error if strata are heterogeneous.
Systematic samples are helpful when elements can be ordered but should avoid cyclical patterns.

Conclusion

Understanding sampling methods, biases, and the differences between sampling error and bias is crucial for effective data collection and analysis.

Studying That Suits You

Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

Description

This quiz focuses on understanding sampling concepts in statistics, including definitions of samples, sampling frames, and parameters of interest. It also addresses the differences between bias and random error, along with identifying sources of bias and their effects on data. Additionally, various sampling designs and their pros and cons will be evaluated.