Statistical Reasoning Flashcards

Sampling Techniques

• Cluster Sampling is used when entire groups are selected; for example, selecting all patients from two randomly chosen companies represents cluster sampling.
• Simple Random Sampling involves randomly selecting individuals or groups without any specific criteria.
• Stratified Sampling requires dividing the population into distinct groups (strata), then taking random samples from each group to ensure diversity.

Effectiveness of Survey Methods

• Cluster Sampling is less effective in homogeneous populations, like diverse neighborhoods, compared to methods like stratified sampling which better captures the variety within the population.

Representation of Study Samples

• In multi-stage sampling, each level of selection contributes to the overall sample. For example, students in only three selected UCLA buildings represent all graduate student apartments in the seven buildings considered.

Bias and Sampling Errors

• Convenience sampling—gathering data from easily accessible individuals—introduces bias, as it fails to represent the broader population accurately.
• Results can misrepresent opinions if not all surveyed individuals have the same opportunity to respond, such as in surveys with low response rates.

Generalizing Findings

• Findings can be generalized to the population if random sampling ensures adequate representation. A valid approach is to use a random student list to select participants.

Data Types

• Quantitative Data refers to numerical data that can be measured, such as the number of traffic tickets.
• Qualitative Data involves descriptive characteristics that cannot be measured numerically.

Parameters vs. Statistics

• A Parameter is a characteristic of a whole population (e.g., the percentage of all vehicles sold that are compact cars).
• A Statistic is derived from a sample of the population and may not represent the whole accurately.

Survey Design Considerations

• Biased survey questions may lead to skewed results. For instance, phrasing that implies a preferred answer can pressure respondents into a specific viewpoint.
• A Systematic Sampling Method involves selecting every nth individual from a list, ensuring a structured approach to sample selection.

Key Survey Findings

• Random sampling is essential to avoid bias and accurately represent the population's opinions, especially in decision-making scenarios like policy changes.

Multistage Sampling and Its Layers

• Multistage sampling combines different sampling methods (like random sampling of states, then universities, and finally students) to create a more manageable and representative sample.
• Disparities between the population of interest and sampling frame can lead to non-representative results, as seen when a study targeting all students only samples those from a specific class.

Voluntary Response Bias

• Surveys based on voluntary responses can result in biased conclusions since they only reflect those who chose to participate, not the entire population's views.

Examples of Stratified Sampling

• Effective stratified sampling involves selecting an equal number of samples from clearly defined groups, ensuring each stratum is proportionally represented in the final analysis.

Test your knowledge of statistical sampling techniques with these flashcards. Learn about cluster sampling, simple random sampling, and stratified sampling through practical scenarios. Perfect for students looking to reinforce their understanding of multistage sampling methods.