The Development Source

Using Statistics Accurately in Proposals

Dec 1, 2013
Small Business
No Comments

Using Statistics Accurately in Proposals

Statistics can be used or abused, especially in proposals.  However, there are steps you can take to use numbers accurately and in ways that promote understanding.

As Darrel Huff has argued in his hilarious classic, How to Lie with Statistics (1954), now back in print, the “secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify.”  Sometimes statistical methods and terms are unwittingly misused, especially in the media.  On other occasions, however, statistics are consciously used to baffle, deceive, legitimize decisions, and bolster authority and power.

To study the lively art of statistical misuse and manipulation, I recommend that you visit the Web site of the Statistical Assessment Service (, a nonprofit organization that examines the ways in which quantitative research is used by the media.  According to STATS, statistical confusion and inaccuracy are thriving in the United States.

The very power and persuasion of quantification obscures the fact that numbers are social and historical artifacts.  They are never abstract, neutral, or value-free.  When we use statistics in proposals, we are doing much more than merely counting or displaying numerical trends.

Usually, numbers in a proposal serve one purpose – to help convince reviewers that we are best qualified to be awarded a contract.  In other words, proposal statistics primarily function as part of a persuasive argument to demonstrate that we are highly experienced and qualified.  Numbers augment our authority and expertise by making us appear “scientific,” rigorous, and credible, whether they are real, false, or misleading.

There are two ways that you can use numbers effectively in proposals.  First, as Huff has argued, non-randomized and small samples are the two most common causes for statistical inaccuracy, especially in the slippery world of claims.  In large data sets, mistaking correlation for cause is a frequent error.  Avoid this approach by accurately describing your samples and be cautious about drawing conclusions from data.

On the positive note, make the accurate pictorial and numerical representation of numbers your top priority.  As Edward Tufte has demonstrated in his The Visual Display of Quantitative Information (1983), too many displays of statistics – charts, graphs, tables, and other representations of quantity – do not depict numbers and numerical trends accurately.

The best approach is to use numbers clearly, accurately, and with a strong sense of caution.  In this way, you will win the confidence of reviewers and construct a more compelling argument in your proposal.

Article source: