STA 2023: Types of Data Vocabulary Flashcards

Vocabulary flashcards covering key terms and definitions from introductory statistics, including parameters, statistics, types of data, levels of measurement, big data, and handling missing data.

Parameter

A numerical measurement describing some characteristic of a population.

Statistic

A numerical measurement describing some characteristic of a sample.

Quantitative Data

Data consisting of numbers representing counts or measurements.

Categorical Data

Data consisting of names or labels (representing categories) that are not numbers representing counts or measurements; also called qualitative or attribute data.

Discrete Data

Data that result when the data values are quantitative and the number of values is finite or 'countable'.

Continuous Data

Data that result from infinitely many possible quantitative values, where the collection of values is not countable.

Nominal Level of Measurement

Level of measurement characterized by data that consist of names, labels, or categories only and cannot be arranged in an ordering scheme.

Ordinal Level of Measurement

Level of measurement involving data that can be arranged in some order, but differences between data values either cannot be determined or are meaningless.

Interval Level of Measurement

Level of measurement involving data that can be arranged in order, and differences between data values are meaningful, but there is no natural zero starting point where none of the quantity is present.

Ratio Level of Measurement

Level of measurement where data can be arranged in order, differences and ratios are meaningful, and there is a natural zero starting point where zero indicates none of the quantity is present.

Ratio Test

A test to determine if data is at the ratio level by asking, 'Does use of the term twice make sense?'

True Zero

A natural zero starting point where zero represents a true absence of the quantity.

Big Data

Data sets so large and complex that their analysis is beyond the capabilities of traditional software tools.

Data Science

A field involving applications of statistics, computer science, and software engineering, along with other relevant fields such as sociology or finance.

Missing Completely at Random

A condition where the likelihood of a data value being missing is independent of its value or any of the other values in the data set.

Missing Not at Random

A condition where a data value is missing because the missing value is related to the reason that it is missing.

Impute Missing Values

The process of substituting replacement values for missing data values, such as substituting the mean for missing numerical values.

Delete Cases

A method for handling missing data by deleting all subjects having any missing values.