Basic Terminology - Chapter 1
A population is the complete collection of all elements (scores, people, measurements, etc.) to be studied.
- A parameter is a numerical measurement describing some characteristic of a population.
- A census is the collection of data from every element in a population.(very difficult to do)
A sample is a subcollection of elements drawn from a population.
- A statistic is a numerical measurement describing some characteristic of a sample.
2 Types of Data - Quantitative vs. Qualitative
- Quantitative data consist of numbers representing counts or measurements. (Tells how many or how much)
- Discrete data can be counted.
- Continuous (numerical) data can be measured.
- Qualitative(or attribute) data can be separated into different categories that are distinguished by some nonnumeric characteristic. (Tells what type)
Levels of Measurement of Data
- Nominal: Categories only. Data cannot be arranged in any order.
- Ordinal: Categories are ordered, but differences cannot be determined or they are meaningless.
- Interval: Differences between values are meaningful, but there is no natural starting point. Ratios have no meaning.
- Ratio: Differences between values are meaningful and there is a natural zero starting point. Ratios are meaningful.
Design of Experiments
- In an observational study, we observe and measure specific characteristics, but we do nothing to the subjects being studied.
- In an experiment, we apply some treatment (do something to the subjects) and then proceed to observe its effects on the subjects.
- 4 Basic Steps in Experimental Design
1. Identify your objective. This involves developing the research question.
2. Collect sample data. Collecting the data in an appropriate manner is crucial.
3. Use a random procedure that avoids bias.
4. Analyze data and form conclusions.
- Confounding occurs in an experiment when the effects from two or more variables cannot be disinguished from each other.
In order, from least to most desirable:
- Convenience Sampling: Use whatever subjects that are readily available.
- Systematic Sampling: Select every nth subject.
- Cluster Sampling: Divide the population area into sections, randomly select a few sections, and then choose all subjects in those sections.
- Stratified Sampling: Classify the population into at least two categories, then draw a sample from each.
- Random Sampling: Each subject in the population has an equal chance of being selected.
A sampling error is the difference between a sample result and the true population result; such an error results from chance sample fluctuations.
A nonsampling error occurs when the sample data are incorrectly collected, recorded, or analyzed (such as by selecting a biased sample, using a defective measuring instrument, or copying the data incorrectly.)