Writing about statistics Guidance for producers

Second Edition

October 2018

Contacts

Good Practice Team

Government Statistical Service

1 Drummond Gate Twitter: @GSSGoodPractice, @UKGSS

London SW1V 2QQ Email us: goodpracticeteam@statistics.gov.uk

Acknowledgements

There are more resources on this topic on the GSS website: http://bit.ly/goodpracticeresources

Writing about statistics Guidance for producers

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Special thanks go to Francesca Allerton who, while on short term secondment to the Good Practice Team in 2017 from the Home Office, was instrumen-

tal in bringing together the main learning points around writing about statistics from across the estate of previous guidance, and developed a new

framework for bringing it together coherently.

Lots of people contributed to the development of this document. Particular acknowledgements are due to the authors of earlier versions of Govern-

ment Statistical Service guidance. This document draws heavily upon and adapts information from ‘Writing about statistics: Guidance for the Govern-

ment Statistical Service on Preparing First Releases’ [1], ‘Standards for Statistical Reports’ [2] and ‘National Statistician’s Guidance: Presentation and

Publication of Official Statistics’ [3], which are superseded by this guidance.

Writing about statistics Guidance for producers

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Foreword

I very much welcome the guidance ‘Writing About Statistics’, a collaboration between the Government Statistical Service’s Good

Practice Team, the Office for Statistics Regulation and colleagues from across the Civil Service.

‘Writing About Statistics’ outlines principles for presenting analysis and data in an insightful way. As analytical professionals, it is

our role to explain statistical information clearly and meaningfully, whilst providing authoritative insights to answer the

important questions that society has. Above all, we need to work as hard as we can to support the users of our numbers so that

they can draw on our work to inform the decisions they have to make.

In line with the Code of Practice for Statistics, the guidance offers practical advice on how to tell an engaging and informative

statistical story. It aims to help to maximise value for our audience, by providing necessary wider context and interpretation to

support the correct use of statistics.

We need to get this right. It is fundamental to our aim of helping Britain make better decisions. I urge you all, as analysts and

producers of statistics, to use this guidance to produce the most relevant and insightful statistical commentary.

John Pullinger

National Statistician

October 2018

Contents

Writing about statistics Guidance for producers

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Introduction ............................................................................................................................................... 5

Top tips for writing about statistics .......................................................................................................... 7

Understand the users and uses of your statistics ..................................................................................... 8

Put the statistics into context ................................................................................................................. 10

Provide interpretation for the statistics .................................................................................................. 13

Present your main messages clearly and concisely ................................................................................ 15

Write clear and informative titles ........................................................................................................... 18

Use plain language .................................................................................................................................. 19

Help users find the information they need ............................................................................................. 21

Consider the online experience .............................................................................................................. 22

Tell users about quality and methods ..................................................................................................... 23

Think beyond bulletins ............................................................................................................................ 25

References ............................................................................................................................................... 27

Resources ................................................................................................................................................ 29

Writing about statistics Guidance for producers

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Introduction

Statistical commentary is required

to bring numbers to life. Commentary should do much more than just

describe the statistics in words. It should help

the user to understand the meaning of

patterns, trends and limitations, and build on

any factual and public information already

known about the subject matter.

Clear, insightful and professionally sound

commentary supports informed decision-

making and democratic debate.

What is good commentary? Good commentary draws attention to

important findings, puts them in context and

provides a clear take away message for users. It

supports and enables the appropriate use of

statistics. It clearly explains issues of quality

and reliability, how these impact on the use of

the statistics and the conclusions that can be

drawn from them.

Good commentary opens up the statistics for re

-use. It ensures users fully understand the

nature of the statistics and the top-line results

they should be able to reproduce if undertaking

further analysis.

Who is this guidance for? This guidance has been developed for

producers of commentary about official

statistics. The guidance may also be helpful for

others who produce and report on statistics.

The guidance has been developed by the

Government Statistical Service Good Practice

Team.

What is the aim of this guidance? The aim of this guidance is to help producers

to write statistical commentary that provides

insight, and is impartial, helpful and accessible

to a range of audiences.

What does this guidance cover? This guidance is not a set of standards, but

rather provides a common approach for

writing about statistics, drawing on

recognised good practice.

We look at how to present a full picture of the

subject, and how factors like structure and

language can impact upon the messages that

readers take away. We discuss the

importance of considering the users of the

statistics when writing commentary. We also

explore how to convey to users what the

statistics mean in practice, whilst keeping the

commentary objective and impartial.

“Statistics and data should be

presented clearly, explained

meaningfully and provide

authoritative insights that serve

the public good.”

Code of Practice for Statistics

UK Statistics Authority, 2018

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Top tips for writing about statistics

Understand the users and

uses of your statistics

Find out who uses your statistics and

what the statistics are used for

Put the statistics into

context Provide neutral and impartial

information about users and

uses, strengths and

limitations, other relevant

statistics, long-term trends

and changes, geographical

comparisons, and why the

statistics have been collected.

Explore patterns, relationships,

causes and effects.

Present main messages

clearly and concisely Don’t try to summarise all of the

findings in the publication: focus on

the main points of interest

Use structure to tell the statistical story

Write clear and informative titles Use plain language

Balance the need for technically

exact but complex terminology

and clarity

Help users find the information they need

A contents page can be helpful for longer releases

Consider the online

experience

Think about how users

access information online

Tell users about quality and methods

Be upfront and specific about important caveats, but

used a tiered approached for more detailed information Think beyond bulletins

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Understand the users and uses of your statistics

A sound understanding of the users

and uses of your statistics is

essential to delivering effective

commentary. Effective commentary

caters well for different audiences.

Find out who uses your statistics and

what the statistics are used for

The audience for statistics is diverse. Your

commentary will be most useful if you have a

clear understanding of your users and how they

draw value from the statistics. What decisions

are taken and arguments made? Are the

statistics re-used in further analysis or

publications? How do different results and

levels of quality affect users’ actions?

Research might be required to identify the wide

range of users and uses of the statistics. There

is guidance for working with users on the GSS

website [4].

Ensure commentary is accessible to all

Government bodies have a legal obligation to

make publications accessible to all [5]. Avoid

barriers to accessibility such as small fonts,

colour contrasts that are hard to distinguish and

complex walls of text.

Think about the requirements of different

audiences, including people with disabilities.

The Government Digital Service blog “Writing

content for everyone” [6] is very helpful.

Engage with users online

StatsUserNet [7] is an interactive website for

communication between users and producers

of official statistics, hosted by the Office for

National Statistics. With over 3,000 individual

members, it is a well-established forum for

online user engagement. Make an effort to

monitor the site regularly and engage with

users’ posts and discussions.

Get feedback from users

When writing about statistics, it is easy to

become too close to the process and unable to

judge whether content is accessible,

understandable and valuable. A second opinion

is usually helpful.

• Ask a colleague or non-specialist in your

department to peer review your writing,

placing themselves as a lay reader without

your expert knowledge.

• Consider inviting wider peer review, either

through a group inside your department, or

initiatives like the Government Statistical

Service’s ‘scrum’ programme. Getting a

perspective from outside your department

can be very valuable.

• Ask your users for feedback. Do they find the

commentary easy to understand? Are the

main messages clear?

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Understand the users and uses of your statistics

Think about what users are trying to

achieve

A ‘jobs to be done’ approach can provide a useful,

simple and quick way of gaining user insight. Think

about what users are trying to achieve with your

statistics, not simply their inherent characteristics.

The Office for National Statistics (ONS) developed

a set of user personas, based on research done

with users of the ONS website. Personas can help

us think about how to present and tailor

commentary for different types of user. The ONS

research identified three broad user groups [8]:

Expert Analysts download spreadsheets into their

own statistical models to create bespoke datasets.

Information Foragers look for statistics to make

practical, strategic business decisions. They want

high level summaries, narratives and charts to

keep up with the latest economic and population

data.

Inquiring Citizens search for the unbiased ‘truth’

about topics raised by the media and political

parties – immigration, house prices, cost of living –

to make informed decisions about their pensions

and investments.

Metadata and data tables, summary report and easy read report from NHS Digital, December 2017, “Health

and care of people with learning disabilities” [9]

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Put the statistics into context

Paint a full picture of the subject of

to help users to understand the

statistics in the context of the wider

world: the economy, society, or the

environment.

Explain who your users are and what the

statistics are used for

Provide context by discussing the users and uses

of the statistics, and describe the types of

decisions made using the statistics. This

information demonstrates the relevance and

public value of the statistics, and provides an

opportunity for other users to express their needs.

Mention both the known and likely uses. It is

acceptable to make assumptions about what the

statistics might be used for.

NHS Digital, March 2018, “Summary Hospital-level

Mortality Indicator (SHMI)” [11]

“The Summary Hospital-level Mortality Indicator

(SHMI) is not a measure of quality of care. A

higher than expected number of deaths should

not immediately be interpreted as indicating

poor performance and instead should be viewed

as a 'smoke alarm' which requires further

investigation. Similarly, an 'as expected' or

'lower than expected' SHMI should not

immediately be interpreted as indicating

satisfactory or good performance.”

Scottish Government, 2016, “Introducing The Scottish

Index of Multiple Deprivation 2016” [10]

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Put the statistics into context

Discuss the findings in the context of

long-term trends and changes

Don’t focus solely on the latest numbers, or on

point-to-point comparisons in isolation. Instead,

give the overall picture, drawing attention to

individual movements only where they add

value to the story.

Do not report on changes without

discussing the context For example, if you report a 2% rise, help the

user understand whether this is typical or

unusual in comparison to previous statistics, to

other countries, or in respect to policy targets.

“In 2016 the UK farmland bird index was less than half its 1970 value. The majority

of this decline occurred between the late 1970s and the 1980s largely due to the

impact of rapid changes in farmland management during this period. More recently

the smoothed index decreased 9% between 2010 and 2015.”

Department for Environment, Food and Rural Affairs, November 2017, “Wild Bird

Population in the UK, 1970 to 2016” [13]

“The Creative Industries accounted for 9.1% of all

UK services imports in 2016, the highest proportion

contributed since 2010. The proportion of the UK

total contributed by the Creative Industries has

generally been growing since 2010, with the

change in the contribution between 2015 and 2016

(up 2.0 percentage points) being higher than

usual.”

Department for Digital, Culture, Media and Sport,

June 2018, “DCMS Sectors Economic Estimates 2016:

Trade” [12]

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Put the statistics into context

Make, or enable users to make,

geographical comparisons

Comparisons may be made between regions,

countries in the UK or internationally. Establish

where equivalent data and publications are

held. Comment on these and include links to

the relevant websites, where appropriate. If

there are differences in methods or definitions,

provide appropriate caveats to avoid misleading

comparisons.

Explain the strengths and limitations of

the statistics in relation to likely uses If there are key issues that affect how the

statistics should be used or interpreted,

mention them up front to support appropriate

use.

Don’t bury important limitations in the

supporting information. Avoid any implication

that the statistics are free from error. Include

descriptions of the main likely errors, their

potential impact on the statistics, and the

implications for use.

Further information about quality and methods

is on page 19 of this guidance.

Be neutral and impartial

Describe policies and targets in factual terms.

Don’t endorse or comment on the effectiveness

of current or past policies, or comment on the

appropriateness of targets.

Departmental logos are helpful for orientation

but be cautious before using the logo or

branding of a government programme to which

the statistics relate. This can carry the risk of

perceived endorsement.

Explain why the statistics have been

collected

Include relevant, factual information about the

policy and operational context. If the statistics

are used to measure policies or targets, list or

provide links to them.

Discuss:

• what is measured

• what the statistics show in relation to the

policies or targets

• any relevant frameworks or indicators

• any relevant previous targets

• why the policy is being monitored

“Indicators are useful tools for summarising

and communicating broad trends. They are

not intended to incorporate all the relevant

information available in the UK. They are

best seen, as their name suggests, as

indicative of wider changes.

The UK biodiversity indicators formed a

major part of the UK’s 5th National Report to

the CBD in 2014, supplemented with other

information relating to UK biodiversity and

implementation of the Strategic Plan for

Biodiversity 2011-2020.

It is expected that the indicators will be

amongst the information used to produce

the 6th National Report to the CBD (due to

be submitted in December 2018). In 2015,

JNCC produced an updated mapping of the

indicators against both global and European

biodiversity targets.”

Department for Environment and Rural Affairs,

August 2017, “UK Biodiversity Indicators

2017” [14]

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Provide interpretation for the statistics

Good commentary should help users

to understand and interpret the

messages from the statistics, and

should be insightful and objective.

Explore relationships, causes and effects

Explore relationships, causes and effects to the

extent that they can be supported by evidence.

Include possible reasons, appropriately justified,

to explain what the statistics show.

It can be challenging to provide insightful

commentary without straying into opinion and

conjecture, but you have an obligation to explain

how any contextual information has been used to

validate your statistics.

Explore potential reasons for the patterns

that you see Do research and keep up to date with the latest

developments in your subject area. Sound

knowledge of your topic and its theoretical

context will help you to interpret the statistics and

add value through your commentary.

“Likewise, the decrease in passenger journeys

on some systems (for example, Docklands Light

Railway and Sheffield Supertram) are likely to

be a result of planned work closure.”

“The total Net Ingredient Cost (NIC) for items

prescribed for alcohol dependence in 2017 was

£4.42 million. This is 9% lower than in 2016 and

breaks the recent trend of successive year on

year increases. The decrease in cost has been

mainly driven by reduced prescriptions items

for Disulfiram.”

“The decline in the number of certificates in Functional Skills is likely due to the changes in funding

rules by the Education and Skills Funding Agency and revised guidance from DfE that post 16 students

who have a grade D/grade 3 in English or maths must now be entered for GCSE resits rather than

Functional Skills. In addition, colleges are also incentivised to enter students with grade E for GCSE as

they gain more credit for distance travelled by improving a GCSE grade than for Functional Skills

attainment.”

Department for Transport, June 2018, “Light Rail and

Tram Statistics England, 2017/18” [18]

OFQUAL, June 2018, “Vocational and other qualifications quarterly: January to March 2018” [15]

NHS Digital, May 2018, “Statistics on Alcohol: England

2018” [17]

“It is reductions from the energy production and manufacturing sectors that have been the strongest

drivers for the long term trend of decreasing emissions, by switching fuel use from coal to gas and the

fitting of flue gas desulphurisation in the remaining coal fired plants in the power sector. The decrease

in SO2 emissions in recent years, with UK emissions falling by 61 per cent between 2012 and 2016, was

largely due to the closure of a number of coal-fired power stations that had reached the end of their

working lifetime. These closures, together with the conversion of a few other coal-fired units to burn

biomass instead, have significantly reduced the overall coal-burning capacity.”

Department for Environment and Rural Affairs, February 2018, “Emissions of air pollutants in the UK, 1970-

2016 [16]

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Provide interpretation for the statistics

Provide insights into any trends

Mention relevant special events or circumstances

that may have affected the statistics. Don’t start

time series at a point that could be perceived as

not being impartial. Similarly, avoid comparisons

of two points that could be perceived as not being

impartial.

Avoid “elevator” commentary that describes every

rise and fall in the numbers. Graphing the series

and pointing out important features will help

when examining trends.

Consult with policy teams and other

specialists

Establish if there have been policy, societal or

economic changes or new initiatives that may

have caused the results observed and reflect

this information in the commentary. Providing

the analysis is evidence-based and impartial,

this can legitimately be done in compliance with

the Code of Practice as part of the quality

assurance process.

Be mindful that what is relevant or

important may change between

releases Don’t just update the numbers into the

narrative of a previous release. Provide a

relevant and insightful story behind the

latest figures, particularly for topics that

become of high national interest or feature

in political debate.

Describe the extent of the uncertainty

in the statistics

Good commentary will help the reader to

understand the extent of uncertainty in the

statistics. It should draw attention to and

make clear the nature and implications of

the uncertainty associated with the statistics.

See the “Communicating Uncertainty and

Change Guidance” on the GSS Policy Store

for more information [20].

Home Office, December

2017, “Hate Crime England

and Wales 2016/17” [19]

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Conveying the main messages from

the statistics is essential to maximise

public value. Focus on the most

important, useful and relevant

messages and present these up front.

Focus on the main points of interest

Take into account your users’ requirements and the

current context. If your statistics say something

important about a current debate, try to

incorporate this information to add public value.

Write accessible and easy to understand

main messages Try to write the main messages so that any user can

understand them. Peer review can really help here.

Update the messages as well as the

numbers

Are the messages from the last reporting period still

the most relevant and newsworthy, or should you

revisit them? Remember that the biggest change

may not be the most important one. Take account

of the current context.

Don’t try to summarise all of the findings in

the publication

Four or five main messages are usually sufficient.

Remember that it is not always necessary

to use bullet points

Other strong layout options are available. Colour

and text boxes can be used to draw attention to

important information.

Main messages from Department for Education,

October 2017, “Pupil absence in schools in England:

autumn 2016 and spring 2017” [21]

Present main messages clearly and concisely

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Present main messages clearly and concisely

Ensure messages can stand alone

Journalists and press offices often use main

messages verbatim. Well drafted messaging

increases the chance of the media identifying

and re-presenting appropriately. Consider

whether the messages can stand alone in a

newspaper article without additional

explanation. If not, they may be taken out of

context.

A number of ONS publications include a

‘Statistician's comment’ to accompany the main

messages and aid correct interpretation.

Use graphs, maps and tables to bring

the main messages to life

This can break up a set of bullet points and adds

interest to the content. You can reinforce

important findings by using annotation and

active titles, which outline the main message

from the visualisation.

Infographics can be a useful tool to present a

number of main messages in an engaging and

informative way.

See “Effective tables and graphs in official

statistics” [22] for more information and

examples of good practice from across

government.

“Statistics should be

accompanied by a clear

description of the main

statistical messages that

explains the relevance and

meaning of the statistics in a

way that is not materially

misleading. They should be

illustrated by suitable data

visualisations, including charts,

maps and tables, where this

helps aid appropriate

interpretation of the statistics.”

Code of Practice for Statistics,

UK Statistics Authority, 2018

Statistician’s comments from Office for National Statistics, March 2018, “Conceptions in England and Wales:

2016 ” [22]

Writing about statistics Guidance for producers

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Use structure to tell the statistical story

The structure of the publication

should help users understand the

story behind the statistics.

Give a summary of the main messages

at the start of the release These should be the points that are most

relevant or interesting to your users and for

public debate. This ensures users come away

with the main messages even if they don’t read

the whole publication.

Use the inverted pyramid structure The inverted pyramid structure is used by

journalists and differs from the traditional style

of academic reporting. The most important

information is presented first. Further detail

and less critical information can be provided

afterwards.

Don’t start with lengthy background

information or technical definitions

Include a short paragraph explaining what the

publication is about. More detailed information

can be placed in an appendix, annex or side

bars.

Consider using descriptive subheadings

Active headings outline the main message

making them more memorable for users.

Only include information which adds to

the statistical story Consider each sentence and whether it adds to

the story. If not, the information can be

presented in side bars or break out boxes

without disrupting the commentary.

Example of side-bars from the Department for Transport:

“Quarterly Bus Statistics: England Q1 (January to March)

2018” [24]; “Road Conditions in England 2017” [25];

“Walking and Cycling Statistics, England: 2016“ [26]

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Write clear and informative titles

Users need clear and informative

titles to help them to identify

whether the statistics are of interest

and relevant to them.

Titles should stand alone

Titles should include this essential information:

• A concise description of the statistics

• The time period covered

• The geographical coverage

• How often the statistics are released

• Whether the statistics are provisional or

final, if applicable.

Avoid ‘producer-focused’ titles

Some titles betray the author's understandable

desire to publicise the work they have done on

a data collection. This may also be a legacy title

used for many years, but changing titles can

and has been done. Aim to convey a user's

perspective of the output. The data source can

be included in a subtitle.

Don’t overload the title with too much

information

If necessary, provide a short paragraph of

additional detail on the front page.

Notice how the Department for Digital, Culture,

Media and Sport makes use of a sidebar to

provide more detail about the time period

covered and the geographical coverage.

Examples of clear and informative titles: Department of Digital, Culture, Media and Sport, November 2017,

“Statistical Release for Reported Treasure Finds 2015 & 2016 (Provisional)” [27]; NHS Digital, December 2017,

“Maternity Services Monthly Statistics, England, July 2017, Experimental Statistics” [28]; Ministry of Housing,

Communities and Local Government, January 2018, “Local authority housing statistics: year ending March 2017,

England” [29]

Writing about statistics Guidance for producers

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Use plain language

The language used in commentary

should be simple, clear and

appropriate for all audiences.

Use plain English

Avoid technical language, jargon and acronyms.

The Government Digital Service has published a

series of helpful blogs about plain English and

clear writing [30].

Be impartial and objective

Avoid sensationalism. Do not use terms that

reflect a value judgement such as “relatively

strong rate”, “very few”, and “only”. Avoid

suggestions of partiality to government, e.g. by

referring to government as “our” or “we”.

“Gross weekly pay in the bottom income

decile was below £276 for full-time

employees”

Is much easier to understand when written

like this:

“One in ten full-time employees earned less

than £276 per week”.

Balance the need for technically exact

but complex terminology and clarity

Users will understand that even with a well

understood term like “unemployment” there

are detailed classificatory decisions taken.

These do not need to be spelt out in the main

messages.

There will be times where more detail is

necessary to avoid risk of confusion between

related concepts.

If technical terms and definitions are

unavoidable, explain them on first use

Some well-known abbreviations and acronyms

may need no explanation, but it is best to be

cautious and to explain any terms that may be

unfamiliar to most readers.

Embedding complex definitions into the main

story makes the language complex and hard to

follow. Side bars or breakout boxes can be used

to explain technical terms and definitions

without disrupting the flow.

Include a glossary of specialist terms

Signpost users to a glossary, but don’t force

them to rely on one. Do not place glossaries at

the front of the document.

Be cautious if using words with specific

meanings in the context of statistics

Take care not to misuse words like “significant”.

In some cases, there may be plausible but

uncertain explanations for patterns in the

statistics. It is important to apply sound

professional judgment. With careful wording,

less certain explanations can also be included.

The GSS guides on Style.ONS can be helpful

here [31].

Words which suggest causality: affect, cause,

consequence, effect, impact

Words which suggest relationship but not

causality: association, correlation,

corresponding, equivalent, parallel

Words which suggest a more provisional

explanation: expect, believe, think, predict,

envisage, forecast

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Use plain language

Be consistent

Use the same terms, abbreviations and units

throughout to help the reader understand and

draw comparisons. For example, don’t switch

between “0.3 million” and “300 thousand”.

Round numbers appropriately Make sure that the level of numerical detail is

appropriate given the precision of the numbers

you are reporting. Figures with lots of detail

give an impression of high accuracy that may be

unwarranted.

Users find it difficult to process long, complex

numbers. For example, use “3.5 million” instead

of “3,546,882”. Use commas to separate out

thousands when writing numbers.

Be concise Write short sentences and paragraphs. Aim for

15-20 words per sentence and one concept per

paragraph. Don’t overload sentences with lots

of numbers.

Use tools to improve readability

Most word processors include grammar

checking tools that can highlight potential

difficulties for readability and clarity.

Online tools like the Hemingway App [32] can help to improve your drafting by

identifying complex sentence structures, phrasing and words. Do not input unpublished

statistics to the Hemingway App.

Writing about statistics Guidance for producers

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Help users find the information they need

Help users quickly and easily

navigate the publication and

identify points of relevance.

A contents page can be helpful for

longer releases A contents list should to be used to give a broad

overview of the structure of the publication. A

long detailed list of tables and figures are off-

putting and detract from the main messages.

Clearly state whether the statistics are

National Statistics or official statistics

Include clear labelling in the release and

supporting documents.

Where the statistics are designated as such,

always use the National Statistics logo. Never

use the logo on outputs that are not National

Statistics.

Also include:

• Timing of the next release

• Copyright terms

• Contact details for the producer

Provide or direct users to relevant

supporting information

Supporting information helps users to understand

and use the statistics correctly. Information should

be readily available from a website landing page

and/or as separate documents.

Provide the underlying and any related

data to enable further analysis

Where possible, include links to supplementary

tables and datasets (e.g. lower geographies, time

series) in a convenient format to allow for the re-

use of the data by others. Consider providing the

data in machine readable open data formats.

Outline the disclosure controls in place. Consider

providing links to any related datasets.

Using a standard template can ensure a

consistent structure

Standard templates can be useful for regular users

who will be able to locate the information they

need quickly and easily. Templates also

demonstrate to users that publications are from a

group of similar or related statistics.

Templates used by the Department for Work and

Pensions: “Universal Credit Statistics Data to 14

December 2017” [33]; and “Employment and Support

Allowance: Work Capability Assessments, Mandatory

Reconsiderations and Appeals Quarterly ESA-WCA

outcomes to September 2016 (MRs to January

2017)” [34]

Writing about statistics Guidance for producers

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Consider the online experience

Many people access statistics online

and on a range of different media.

This results in a very different user

experience in comparison to

reading a printed publication.

Think about reading speed

Reading tends to be slower online, but people

expect quicker results and spend little time on a

page. Clear identification of the main messages

and being able to easily scan content is even

more important.

Place the essential information and key

words on the top left

Our eyes move across web pages from left to

right, top to bottom, in an F-pattern. This places

most attention on the top left of the page. Use

the right and lower part of the page for

supporting information that is not essential for

the main story.

Further guidance on writing for the web is

available on the Style.ONS website [31].

Use web analytics to gain user insight

Government departments are increasingly

moving from PDF to HTML formats. HTML

formats can be restrictive in terms of what can

be published, for example some visualisations

cannot be published on GOV.UK. However,

HTML does allow producers to use website data

to gain insight into how users access and

navigate in publications.

Analysis of visitors to the ONS website found

that only 20% of users scroll a quarter of the

way down a bulletin, and 53% of people who

land on a bulletin page leave the site

immediately. Bulletins take 9.5 times longer to

read than people actually spend on the page

[35].

Writing about statistics Guidance for producers

Page 22

Tell users about quality and methods

Commentary should be supported

by information that describes the

quality of the statistics and the

methods used to derive them.

Be upfront about any important caveats

Any caveats that arise because of the quality of

the statistics or the methods used should be

presented early on in the publication. However,

ensure that these details do not dilute or

obscure the main messages.

Use progressive disclosure

Adopt a tiered approach with different levels of

information available for different users.

Think about user personas, In general, non-

technical users will not need to know the

detailed methods involved to use the numbers

with confidence.

Detailed quality and methods information

should be provided in an appendix or annex for

technical users.

Be specific

Avoid general statements about the quality of

the statistics. Instead, focus on how quality and

methods impact on use.

Explain complex concepts

When discussing confidence intervals and other

quality measures use a plain English

explanation. Make sure that such explanations

are as easy as possible to understand and not

overly detailed. Ask a colleague or non-expert

to peer review the explanation.

Explanation of confidence intervals from Department for Work

and Pensions, November 2017, “Fraud and Error in the Benefit

System, Final 2016/17 Estimates” [36]

“The quality of the statistics

and data, including their

accuracy and reliability,

coherence and comparability,

and timeliness and

punctuality, should be

monitored and reported

regularly.”

Code of Practice for Statistics,

UK Statistics Authority, 2018

Writing about statistics Guidance for producers

Page 23

Tell users about quality and methods

Explain the statistics are initial

estimates if normally subject to later

revision

Include a revisions statement which outlines:

• When the statistics are likely to be revised

• The extent and direction of any likely

revision (take care to avoid conjecture)

• A link to a published Revisions Policy

relating to the statistics.

Smaller revisions are a measure of reliability.

However, small revisions do not necessarily

mean that the statistics are accurate.

To prevent confusion or the use of incorrect

figures, ensure only the latest version of a

revised dataset is available. Explain the nature

and extent of revisions, and how these

revisions affect the interpretation of the

statistics.

Report quality against the European

Statistical System’s quality dimensions

Relevance is the degree to which a statistical

product meets user needs in terms of content

and coverage.

Accuracy and Reliability is how close the

estimated value in the initial and final outputs

are to the true result.

Timeliness and Punctuality describes the time

between the date of publication and the date

to which the data refers, and the time between

the actual publication and the planned

publication of a statistic.

Accessibility and Clarity is the quality and

sufficiency of metadata, illustrations and

accompanying advice, and the quality and

sufficiency of metadata, illustrations and

accompanying advice.

Coherence and Comparability is the degree to

which data derived from different sources or

methods, but that refers to the same topic, is

similar, and the degree to which data can be

compared over time and domain, for example,

geographic level.

Further information about the quality

dimensions and on quality reporting can be

found on the GSS website [38].

“Scheduled revisions, or

unscheduled corrections that

result from errors, should be

explained alongside the

statistics, being clear on the

scale, nature, cause and impact.”

Code of Practice for Statistics,

UK Statistics Authority, 2018

Break in time series, NHS Digital, May 2018, “Statistics

on Alcohol, England” [17]

Writing about statistics Guidance for producers

Page 24

Think beyond bulletins

All of the statistical outputs made

available to users should include

appropriate and accessible

commentary.

Adapt commentary for different users

and outputs

Commentary in statistical bulletins may not be

appropriate for all types of users. Good

commentary can be adapted from bulletins and

used elsewhere.

Use main messages from your commentary for

policy colleagues, social media outputs,

infographics and board reports—but adapt to

suit these different users.

Use social media alongside statistical

releases

Social media can help reach a wide audience

and convey headline messages quickly.

The Government Digital Service’s ‘Social Media

Playbook’ provides comprehensive guidance on

using social media in government [39].

Welsh Government, June 2018, Twitter feed [41]

OFSTED’s Chief Statistician and National Director blog

provides more information about the data used and

additional context for readers [40].

Writing about statistics Guidance for producers

Page 25

[1] UK Statistics Authority, January 2012, Writing about statistics: Guidance for the Government Statistical Service on Preparing First Releases https://

www.statisticsauthority.gov.uk/wp-content/uploads/2016/11/Writing-About-Statistics-National-Statisticians-Guidance.pdf

[2] UK Statistics Authority, November 2012, Standards for Statistical Reports https://www.statisticsauthority.gov.uk/gsspolicy/standards-for-statistical-reports/

[3] National Statistician’s Guidance: Presentation and Publication of Official Statistics https://www.statisticsauthority.gov.uk/wp-content/uploads/2016/11/

Presentation-and-Publication-of-Official-Statistics-National-Statisticians-Guidance.pdf

[4] GSS Good Practice Team, accessed 20.02.2018, Working with users https://gss.civilservice.gov.uk/statistics/working-with-users/

[5] Equality Act 2010 (UK) Section 149 Public sector equality duty http://www.legislation.gov.uk/ukpga/2010/15/section/149

[6] Government Digital Service, February 2016, Writing Content for Everyone https://gds.blog.gov.uk/2016/02/23/writing-content-for-everyone/

[7] Stats User Net http://www.statsusernet.org.uk/home

[8] Office for National Statistics, April 2014, The Persona touch https://digitalblog.ons.gov.uk/2014/04/02/the-persona-touch/

[9] NHS Digital, December 2017, Health and care of people with learning disabilities https://digital.nhs.uk/data-and-information/publications/statistical/health

-and-care-of-people-with-learning-disabilities/health-and-care-of-people-with-learning-disabilities-experimental-statistics-2016-to-2017

[10] Scottish Government, 2016, Introducing The Scottish Index of Multiple Deprivation 2016 http://www.gov.scot/Resource/0050/00504809.pdf

[11] NHS Digital, March 2018, Summary Hospital-level Mortality Indicator (SHMI) - Death associated with hospitalisation, England, October 2016-September

2017 https://digital.nhs.uk/data-and-information/publications/clinical-indicators/shmi/current

[12] Department for Digital, Culture, Media and Sport, June 2018, DCMS Sectors Economic Estimates 2016: Trade Economics https://

assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/714021/

DCMS_Sectors_Economic_Estimates_2016_Trade_final.pdf

[13] Department for Environment, Food and Rural Affairs, November 2017, Wild Bird Population in the UK, 1970 to 2016 https://www.gov.uk/government/

uploads/system/uploads/attachment_data/file/661681/UK_Wild_birds_1970_2016_FINAL_.pdf

[14] Department for Environment and Rural Affairs, August 2017, UK Biodiversity Indicators 2017 http://jncc.defra.gov.uk/pdf/UKBI_2017.pdf

[15] OFQUAL, June 2018, Vocational and other qualifications quarterly: January to March 2018 https://www.gov.uk/government/statistics/vocational-and-other

-qualifications-quarterly-january-to-march-2018

References

Writing about statistics Guidance for producers

Page 26

[16] Department for Environment, Food and Rural Affairs, February 2018, Emissions of air pollutants in the UK, 1970 to 2016 https://

assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/681445/

Emissions_of_air_pollutants_statistical_release_FINALv4.pdf

[17] NHS Digital, May 2018, Statistics on Alcohol, England 2018 https://digital.nhs.uk/data-and-information/publications/statistical/statistics-on-alcohol/2018

[18] Department for Transport, June 2018, Light Rail and Tram Statistics, England: 2017/18 https://assets.publishing.service.gov.uk/government/uploads/

system/uploads/attachment_data/file/720315/light-rail-and-tram-statistics-england-2018.pdf

[19] Home Office, October 2017, Hate Crime, England and Wales, 2016/17 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/

attachment_data/file/652136/hate-crime-1617-hosb1717.pdf

[20] GSS Good Practice Team, November 2014, Communicating uncertainty and change: guidance for official statistics producers https://

www.statisticsauthority.gov.uk/wp-content/uploads/2016/11/Communicating-uncertainty-and-change-Guidance-for-Official-Statistics.pdf

[21] Department for Education, October 2017, Pupil absence in schools in England: autumn 2016 and spring 2017 https://www.gov.uk/government/uploads/

system/uploads/attachment_data/file/652689/SFR55_2017_text.pdf

[22] GSS Good Practice Team, February 2018, Effective tables and graphs in official statistics: Guidance for producers https://www.statisticsauthority.gov.uk/wp

-content/uploads/2016/11/Effective-charts-and-tables-in-official-statistics-Version-2.0.pdf

[23] Office for National Statistics, March 2018, Conceptions in England and Wales: 2016 https://www.ons.gov.uk/peoplepopulationandcommunity/

birthsdeathsandmarriages/conceptionandfertilityrates/bulletins/conceptionstatistics/2016

[24] Department for Transport, June 2018, Quarterly Bus Statistics: England Q1 (January to March) 2018 https://assets.publishing.service.gov.uk/government/

uploads/system/uploads/attachment_data/file/715419/quarterly-bus-statistics-january-to-march-2018.pdf

[25] Department for Transport, January 2018, Road conditions in England: 2017 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/

attachment_data/file/674577/road-conditions-in-england-2017.pdf

[26] Department for Transport, January 2018, Walking and Cycling Statistics, England: 2016 https://assets.publishing.service.gov.uk/government/uploads/

system/uploads/attachment_data/file/674577/road-conditions-in-england-2017.pdf

References

Writing about statistics Guidance for producers

Page 27

[27] Department of Digital, Culture, Media and Sport, November 2017, Statistical Release for Reported Treasure Finds 2015 & 2016 (Provisional) https://

www.gov.uk/government/uploads/system/uploads/attachment_data/file/661284/Statistical_Release_for_Reported_Treasure_Finds_2015-2016.pdf

[28] NHS Digital, December 2017, Maternity Services Monthly Statistics, England, July 2017, Experimental Statistics https://digital.nhs.uk/media/34331/

Maternity-Services-Monthly-Statistics-England-July-2017-Experimental-statistics-Executive-Summary/default/msms-jul17-exp-rep

[29] Ministry of Housing, Communities and Local Government, January 2018, Local authority housing statistics: year ending March 2017, England https://

www.gov.uk/government/uploads/system/uploads/attachment_data/file/674337/

Local_Authority_Housing_Statistics_England_year_ending_March_2017.pdf

[30] Government Digital Service blog https://gds.blog.gov.uk/

[31] Style guides Style.ONS https://style.ons.gov.uk/

[32] Hemingway Editor http://www.hemingwayapp.com/

[33] Department for Work and Pensions, January 2018,Universal Credit Statistics Data to 14 December 2017 https://www.gov.uk/government/uploads/system/

uploads/attachment_data/file/675454/universal-credit-statistics-to-14-dec-2017.pdf

[34] Department for Work and Pensions, December 2017,Employment and Support Allowance: Work Capability Assessments, Mandatory Reconsiderations and

Appeals Quarterly ESA-WCA outcomes to September 2016 (MRs to January 2017) https://www.gov.uk/government/uploads/system/uploads/

attachment_data/file/667373/esa-wca-summary-dec-17.pdf

[35] Office for National Statistics (unpublished data)

[36] Department for Work and Pensions, November 2017, Fraud and Error in the Benefit System, Final 2016/17 Estimates https://www.gov.uk/government/

uploads/system/uploads/attachment_data/file/664827/fraud-and-error-stats-release-2016-17-final-estimates.pdf

[37] Government Statistical Service Quality Guidelines https://gss.civilservice.gov.uk/statistics/quality/quality-guidelines/

[38] Government Digital Service, Social Media Playbook https://gdsengagement.blog.gov.uk/playbook/

[39] OFSTED, June 2018, Deprivation, ethnicity and school inspection judgments https://assets.publishing.service.gov.uk/government/uploads/system/uploads/

attachment_data/file/674577/road-conditions-in-england-2017.pdf

[40] Welsh Government, June 2018, Twitter feed https://twitter.com/WelshGovernment/status/1012338378728230912

References

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Page 28

United Nation Economic Commission for Europe (UNECE), 2009, Making Data Meaningful http://www.unece.org/stats/documents/writing/

UK Statistics Authority, February 2018, Code of Practice for Statistics

HM Treasury, March 2015, The Aqua Book: guidance on producing quality analysis for government https://www.gov.uk/government/uploads/system/

uploads/attachment_data/file/416478/aqua_book_final_web.pdf

David Spiegelhalter, Understanding Uncertainty blog https://understandinguncertainty.org/davidsblog

Rieser, V., 2017, Women listen and men look? How to best communicate risk to support decision making https://understandinguncertainty.org

Galesic, M and Garcia-Retamero, R., 2010, Statistical numeracy for health: a cross-cultural comparison with probabilistic national samples, Arch Intern

Med. 2010;170(5):462–468. doi:10.1001/archinternmed.2009.481 .

Royal Statistical Society and the Inns of Court College of Advocacy, 2017, Statistics and probability for advocates: Understanding the use of satistical

evidence in courts and tribunals.

Resources