Statistics Chapter 1 Test Flashcards

Statistics Overview

• Statistics encompasses methods for designing experiments and collecting data, as well as organizing, summarizing, analyzing, and interpreting this data to draw conclusions.

Key Terminology

• Population: Total collection of all elements (scores, people, measurements) targeted for study.
• Census: Data collection that covers every element within a population.
• Sample: A subset of elements selected from a population for analysis.

Measurements

• Parameter: A numerical value that describes a characteristic of a population.
• Statistic: A numerical value that describes a characteristic of a sample.

Data Types

• Quantitative Data: Numeric data representing counts or measurements.
• Qualitative Data: Data categorized by non-numeric characteristics.
• Discrete Data: Derived from finite or countable values, including whole numbers.
• Continuous Data: Associated with a continuous scale, allowing for an infinite number of possible values without gaps.

Levels of Measurement

• Nominal Level: Data categorized by names or labels without a natural ordering.
• Ordinal Level: Data can be ordered but differences may be meaningless or undetermined.
• Interval Level: Offers meaningful differences between values but lacks a true zero point.
• Ratio Level: Similar to interval but includes an inherent zero, making both differences and ratios meaningful.

Survey and Study Methods

• Self-Selected Survey: Participants choose to be included in the survey.
• Observational Study: Involves observing subjects without manipulating variables.
• Experiment: Involves applying a treatment to subjects and observing outcomes.

Sampling Techniques

• Confounding: Difficulty in distinguishing effects of two or more variables simultaneously.
• Random Sample: Each member of a population has an equal chance of selection.
• Simple Random Sample: Every possible sample of a specified size has the same chance of being chosen.
• Stratified Sampling: Population divided into strata based on shared characteristics, with a sample drawn from each stratum.
• Systematic Sampling: Selecting a starting point and every kth member thereafter.
• Cluster Sampling: Dividing the population into clusters, randomly selecting clusters, and including all members from chosen clusters.
• Convenience Sampling: Using readily available results without systematic selection.

Sampling Errors

• Sampling Error: Discrepancy between sampled results and actual population results due to random chance.
• Nonsampling Error: Errors resulting from incorrect data collection, recording, or analysis, including biases in sample selection or measurement errors.