Overview
This lecture outlines the four key steps in the statistical process, focusing on problem identification, data collection, data description, and inference.
Steps in the Statistical Process
1. Identify the Problem
- The first step is to clearly define the question the researcher wants answered.
- It is essential to specify the population the study applies to.
2. Collect the Data
- Data collection is crucial; improper data collection leads to meaningless results.
- Studying the entire population is often difficult or expensive, so a subset called a sample is usually used.
3. Describe the Data
- Describing data helps understand what the data indicates and guides the choice of inferential methods.
4. Perform Inference
- Inference involves generalizing sample results to the entire population.
- Always include a measure of reliability with the reported results.
Key Terms & Definitions
- Population — The entire group to which the study aims to apply its findings.
- Sample — A subset of the population used to represent the whole in the study.
- Inference — The process of drawing conclusions about a population based on sample data.
- Reliability — A measure indicating how much trust can be placed in the results.
Action Items / Next Steps
- Review and memorize the four steps of the statistical process.
- Practice identifying populations, samples, and appropriate inferential methods in example problems.