Producers of statistics and data should use the best available methods and recognised standards, and be open about their decisions.
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.
|A resource for official statistics producers to develop their knowledge and understanding of the broad range of methodological approaches used across the Government Statistical Service (GSS).||GSS methodology webpage||GSS|
|Guidelines that aid producers in the production of official statistics. They include information about measuring and reporting statistical quality, quality assurance, and reporting metadata.||GSS Quality Guidelines||GSS|
|Guidance that sets out the UK Statistics Authority (UKSA) policy on experimental statistics. Experimental statistics are a subset of new developed or innovative official statistics undergoing evaluation. The guidance sets out when to use the experimental statistics label, when to introduce experimental statistics, and removal of the experimental statistics label.||Guidance on Experimental Statistics||GSS|
|National Statistician’s guidance on interpreting and implementing the principles and practices of Code which relate to the quality and methodological basis of their official statistics. This document is scheduled for revision in 2018 to reflect version 2.0 of the Code.||National Statistician’s Guidance: Quality, Methods, and Harmonisation (2009)||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|
|A one-page guide for authors and reviewers on how to conduct an effective peer review of statistics. It contains information on why a peer review is helpful, what the process can look like, and questions to ask.||Hints and Tips: Conducting a Peer Review||GSS|
|Information on a range of statistical classifications and standards, including the UK Standard Industrial Classification of Economic Activities, the Standard occupational classification, Economic statistics classifications, and other national and international classifications.||Statistical classifications||ONS|
|Guidance on collecting and classifying data on ethnic group, national identity, religion, and sexual identity, and an overview of ONS’s work on gender identity.||Guidance on measuring equality||ONS|
|Guidance that provides a common approach to aid the clear communication of uncertainty and change. It can be applied to all sources of information, including surveys, censuses, administrative data and other sources, as well as estimates derived from a combination of these. It includes examples of good practice, as well as standard wording to be used when appropriate. This document is scheduled for revision in 2018 to reflect version 2.0 of the Code.||Communicating Uncertainty and Change: Guidance for Official Statistics Producers||GSS|
|Guidance on how to use a standard and straightforward way of assessing comparability of their statistics with other parts of the UK. It also allows users to better understand what is comparable so that they can quickly get to the numbers they want.||Guidance on comparing official statistics across the UK||GSS|
|Guidance for government analysts on when and how to use quota sampling. The target audience is government analysts involved with quota surveys – whether commissioning research, designing and running surveys, or interpreting the results to inform policy colleagues.||Government Social Research (GSR) quota sampling guidance||GSR|
|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. It recognises that reliable and objective information is crucial for decision making.||UN Fundamental Principles of Official Statistics||UNECE|