 Statistical Language Glossary |
Absolute frequency
An absolute frequency is the total number of times a particular value for a variable actually occurs.
See: Describing Frequencies
Administrative data
Administrative data are collected as part of the day to day processes and record keeping of organisations.
See: Data Sources
Bar chart
A bar chart is a type of graph in which each column (plotted either vertically or horizontally) represents a categorical variable or a discrete ungrouped numeric variable.
See: Frequency Distribution
Categorical variable
Categorical variables have values that describe a 'quality' or 'characteristic' of a data unit, like 'what type' or 'which category'.
See: What are Variables?
Causation
Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events. This is also referred to as cause and effect.
See: Correlation and Causation
Census (complete enumeration)
A census is a study of every unit, everyone or everything, in a population.
See: Census and Sample
Classifications
Classifications are used to collect and organise information into categories with other similar pieces of information.
See: What are Standards?
Comparability
Comparability is the ability to validly compare statistics that have been collected over time, or from different sources.
See: What are Standards?
Confidence interval
A confidence interval is a range in which it is estimated the true population value lies.
See: Measures of Error
Confidentiality
Confidentiality refers to the obligation of organisations that collect information to keep the information they are entrusted with secret.
See: Confidentiality
Continuous variable
Continuous variables can take a value based on a measurement at any point along a continuum.
See: What are Variables?
Correlation
Correlation is a statistical measure (expressed as a number) that describes the size and direction of a relationship between two or more variables.
See: Correlation and Causation
Cyclic effect
A cyclic effect is any regular fluctuation in daily, weekly, monthly or annual data.
See: Time Series Data
Data
Data are measurements or observations that are collected as a source of information.
See: What are Data?
Data item (or variable)
A data item is a characteristic of a data unit which is measured or counted, such as height, country of birth, or income.
See: What are Data?
Dataset
A dataset is a complete collection of all observations.
See: What are Data?
Data unit
A data unit is one entity (such as a person or business) in the population being studied, about which data are collected.
See: What are Data?
Data visualisation
Data visualisation involves the visual presentation of data to communicate the stories contained in the dataset.
See: Data Visualisation
Descriptive (or summary) statistics
Descriptive statistics summarise the raw data and allow data users to interpret a dataset more easily.
See: What are Statistics?
Discrete variable
Discrete variables can take a value based on a count from a set of distinct whole values.
See: What are Variables?
Error (Statistical error)
Statistical error describes the difference between a value obtained from a data collection process and the 'true' value for the population.
See: Types of Error
Estimate
An estimate is a value that is inferred for a population based on data collected from a sample of units from that population.
See: Estimate and Projection
Flow series
A flow series is a series which is a measure of activity over a given period.
See: Time Series Data
Frequency
The frequency is the number of times a particular value for a variable (data item) has been observed to occur.
See: Describing Frequencies
Frequency distribution
Frequency distributions are used to organise and present frequency counts in a summary form so that the information can be interpreted more easily.
See: Frequency Distribution
Histogram
A histogram is a type of graph in which each column represents a numeric variable, in particular that which is continuous and/or grouped.
See: Frequency Distribution
Interquartile range (IQR)
The interquartile range (IQR) is the difference between the upper (Q3) and lower (Q1) quartiles, and describes the middle 50% of values when ordered from lowest to highest.
See: Measures of Spread
Irregular effect
An irregular effect is any movement that occurred at a specific point in time, but is unrelated to a season or cycle.
See: Time Series Data
Inferential statistics
Inferential statistics are used to infer conclusions about a population from a sample of that population.
See: What are Statistics?
Mean
The mean is the sum of the value of each observation in a distribution divided by the number of observations.
See: Measures of Central Tendency
Measures of central tendency (centre or central location)
A measure of central tendency (also referred to as measures of centre or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution.
See: Measures of Central Tendency
Measures of shape
Measures of shape describe the distribution of the data within a dataset.
See: Measures of Shape
Measures of spread
Measures of spread are descriptive statistics that show how similar or varied the set of observed values are for a particular variable (data item).
See: Measures of Spread
Median
The median is the middle value in distribution when the values are arranged in ascending or descending order.
See: Measures of Central Tendency
Metadata
Metadata is the information that defines and describes data.
See: What is Metadata?
Mode
The mode is the most commonly occurring value in a distribution.
See: Measures of Central Tendency
Nominal variable
Nominal variables take on values that are not able to be organised in a logical sequence.
See: What are Variables?
Non-random (non-probability) sample
In a non-random (or non-probability) sample some units of the population have no chance of selection, the selection is non-random, or the probability of their selection can not be determined.
See: Census and Sample
Non-sampling error
Non-sampling error is caused by factors other than those related to sample selection.
See: Types of Error
Normal distribution
A normal distribution is a true symmetric distribution of data item values.
See: Measures of Shape
Numeric variable
Numeric variables have values that describe a measurable quantity as a number, like 'how many' or 'how much'.
See: What are Variables?
Observation
An observation is an occurrence of a specific data item that is recorded about a data unit.
See: What are Data?
Ordinal variable
Ordinal variables take on values that can be logically ordered or ranked.
See: What are Variables?
Original time series
An original time series shows the actual movements in the data over time.
See: Time Series Data
Outlier
An outlier has a value which is very different to the rest of the distribution.
See: Measures of Central Tendency
Percentage
A percentage expresses the share of one value for a variable in relation to the whole population as a fraction of one hundred.
See: Describing Frequencies
Population
A population is any complete group with at least one characteristic in common.
See: What is a Population?
Projection
A projection indicates what the future changes in a population would be if the assumptions about future trends actually occur.
See: Estimate and Projection
Proportion
A proportion describes the share of one value for a variable in relation to a whole.
See: Describing Frequencies
Qualitative data
Qualitative data are measures of 'types' and may be represented by a name, symbol, or a number code.
See: Quantitative and Qualitative Data
Quantitative data
Quantitative data are measures of values or counts and are expressed as numbers.
See: Quantitative and Qualitative Data
Quartiles
Quartiles divide an ordered dataset into four equal parts, and refer to the values of the point between the quarters.
See: Measures of Spread
Ratio
A ratio compares the frequency of one value for a variable with to another value for the variable.
See: Describing Frequencies
Rate
A rate is a measurement of one value for a variable in relation to another measured quantity.
See: Describing Frequencies
Random (probability) sample
In a random (or probability) sample each unit in the population has a chance of being selected, and this probability can be accurately determined.
See: Census and Sample
Range
The range is the difference between the smallest value and the largest value in a dataset.
See: Measures of Spread
Relative frequency
A relative frequency describes the absolute frequency of a particular value for a variable in relation to the total number of values for that variable.
See: Describing Frequencies
Relative standard error (RSE)
The relative standard error (RSE) is the standard error expressed as a proportion of an estimated value.
See: Measures of Error
Respondent
A respondent provides data about oneself as a unit, or as a representative of another unit in a population.
See: Data Sources
Sample (partial enumeration)
A sample is a subset of units in a population, selected to represent all units in a population of interest.
See: Census and Sample
Sampling error
Sampling error occurs solely as a result of using a sample from a population, rather than conducting a census (complete enumeration) of the population.
See: Types of Error
Seasonal effect
A seasonal effect is any variation in which is dependent on a particular time of year.
See: Time Series Data
Seasonally adjusted series
A seasonally adjusted series involves estimating and removing the cyclical and seasonal effects from the original data.
See: Time Series Data
Skewness (skewed distribution)
Skewness is the tendency for the values to be more frequent around the high or low ends of the x-axis.
See: Measures of Shape
Standard deviation
The standard deviation measures the spread of the data around the mean.
See: Measures of Spread
Standard error (SE)
The standard error (SE) indicates the amount of variation between any estimated value based on a sample and the true value for the population.
See: Measures of Error
Statistics
A statistic is a value that has been produced from a data collection and can take the form of a summary measure, an estimate or projection.
See: What are Statistics?
Statistical standard
A statistical standard is a set of rules used to standardise the way data are collected and statistics are produced.
See: What are Standards?
Stock series
A stock series is a measure of certain attributes at a point in time and can be thought of as “stock takes”.
See: Time Series Data
Survey
A survey involves collecting information from every unit in the population (a census), or from a subset of units (a sample) from the population.
See: Data Sources
Time series
A time series is a collection of observations of well-defined data items obtained through repeated measurements over time.
See: Time Series Data
Trend series
A trend series is a seasonally adjusted series that has been further adjusted to remove irregular effects and 'smooth' out the series to show the overall 'trend' of the data over time.
See: Time Series Data
Variable (data item)
A variable is any characteristic, number, or quantity that can be measured or counted.
See: What are Variables?
Variance
The variance measures the spread of the data around the mean.
See: Measures of Spread
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