Producers of statistics and data should use the best available methods and recognised standards, and be open about their decisions.
What you should commit to
Q2.1 Methods and processes should be based on national or international good practice, scientific principles, or established professional consensus.
Q2.2 Statistics, data and metadata should be compiled using recognised standards, classifications and definitions. They should be harmonised to be consistent and coherent with related statistics and data where possible. Users should be provided with reasons for deviations from these standards and explanations of any related implications for use.
Q2.3 Statistics producers should be transparent about methods used, giving the reasons for their selection. The level of detail of the explanation should be proportionate to the complexity of the methods chosen and reflect the needs of different types of users and uses.
Q2.4 Relevant limitations arising from the methods and their application, including bias and uncertainty, should be identified and explained to users. An indication of their likely scale and the steps taken to reduce their impact on the statistics should be included in the explanation.
Q2.5 Producers of statistics and data should provide users with advance notice about changes to methods, explaining why the changes are being made. A consistent time series should be produced, with back series provided where possible. Users should be made aware of the nature and extent of the change.
Q2.6 Statistics producers should collaborate with topic and methods experts and producers of related statistics and data wherever possible.
Guidance and resources
|This guide sets out the Office for Statistics Regulation’s (OSR) expectations regarding the production and handling of experimental statistics, a subset of official statistics going through development and evaluation.||Experimental statistics – official statistics in development||OSR|
|This OSR guide sets out examples of the principles in the Code that producers need to adhere to when making changes to statistical methods, in order to remain code compliant.||Guidance for producers when making changes to statistical methods||OSR|
|A resource for official statistics producers to develop their knowledge and understanding of the broad range of methodological approaches used across the GSS.||GSS methodology webpage||GSS|
|This guidance supports statistics producers in meeting the quality requirements of the UK Code of Practice for Statistics and understanding of Eurostat’s European Statistics Code of Practice, which sets out five dimensions for measuring the quality of statistical outputs.||Quality statistics in government||GSS|
|This strategy aims to improve statistical quality across the Government Statistical Service (GSS) to produce statistics that serve the public good. It sets out what the GSS should be doing to improve the quality of its statistics and manage the processes surrounding their production.||GSS Quality Strategy||GSS|
|These case studies provide examples of successful improvements to the quality of GSS statistics.||GSS Quality Strategy case studies||GSS|
|Guidance that provides practical advice on how to communicate quality, uncertainty and change for different types of statistics and for a range of audiences.||Communicating quality, uncertainty and change||GSS|
|A webpage with links to a series of guidance documents on harmonisation, including what harmonisation is and its aims, the Harmonisation Handbook and the GSS Harmonised Principles.||Harmonisation within the GSS webpage||GSS|
|This Government Statistical Service (GSS) guidance sets out when to use the experimental statistics label, when to introduce experimental statistics, and removal of the experimental statistics label.||Publishing experimental statistics||GSS|
|Guidance for government analysts on when and how to use quota sampling.||Quota sampling guidance||GSS|
|This cross-government review contains contributed articles on state-of-the-art data linking methods and makes recommendations for government data linkage.||Joined up data in government: the future of data linking methods||ONS and Government Analysis Function|
|Information on a range of statistical classifications and standards, including the UK Standard Industrial Classification of Economic Activities and the Standard Occupational Classification.||Statistical classifications||ONS|
|Guidance on collecting and classifying data on ethnic group, national identity, religion, and sexual identity, and an overview of the Office for National Statistics' (ONS) work on gender identity.||Guidance on measuring equality||ONS|
|The European Statistics Code of Practice, adopted by the European Statistical System (ESS), aims to ensure that statistics produced within the ESS are not only relevant, timely and accurate but also comply with principles of professional independence, impartiality and objectivity. The Code of Practice for Statistics is aligned with the ESS Code of Practice.||European Statistics Code of Practice (2011 edition)||Eurostat|
|Information on all main international statistical methods and classifications used by the United Nations (UN).||UN international classifications||UN|
|A UN list of agreed international statistical principles and good practice tips that will enhance the functioning of the international statistical system. The Code of Practice for Statistics is aligned with these principles.||UN principles governing international statistical activities||UN|
|The United Nations Economic Commission for Europe’s (UNECE) Fundamental Principles of Official Statistics sets out the standards of official statistics that have been adopted at all levels of the UN.||UN Fundamental Principles of Official Statistics||UNECE|