How valid research improves the authority of your content

How valid research improves the authority of your content

Content marketing can be bland without original insight and commentary. Research provides that originality. But it has to be valid research or you risk losing your authority.

So how do you check your research is valid? I’ve been carrying out market research for more years than I care to remember. Fortunately, there is a formula you can use to achieve valid research and repeatable results. But before we dive into that, a word about the ‘Rule of 100’.

The Rule of 100 states if you get 100 responses to a survey, the ranking of answers will remain the same whether you get an additional 1,000 or 10,000 responses. The percentage difference between the first and second placed answer may change, but the top answer will remain the top answer.

I was initially sceptical of the Rule of 100, but I have seen so many examples where it is true that I now have to accept it has a place in market research. The caveat is that questions must be simple and the number of answers limited to less than 10. So if you are going to use the Rule of 100 think carefully about your questions and answers.

You should also accept that if there is just one percentage point difference between first and second place, then those positions could be easily reversed.

One of the main reasons I believe the Rule of 100 is useful in market research is sometimes it’s extremely difficult to get more than 100 answers from certain audiences.

But as a marketing scientist, I would always recommend using a formula and calculate the validity of your research. Below are 3 Tables showing how many survey responses you need for your research to be considered valid. They are based on 3 different confidence levels.

Table I - Valid research sample sizes; 90% confidence

Valid Sample Sizes

Confidence: 90%
Error margin: 1% 3% 5% 8%
Population Sample size
100 99 89 74 52
500 466 301 176 88
1,000 872 430 214 96
10,000 4,036 700 264 105
15,000 4,662 716 266 105
30,000 5,520 734 269 106
100,000 6,336 746 270 106
500,000 6,674 751 271 106
1,000,000 6,719 751 271 106


Table II - Valid research sample sizes; 95% confidence

Valid Sample Sizes

Confidence: 95%
Error margin: 1% 3% 5% 8%
Population Sample size
100 99 92 80 61
500 475 341 217 116
1,000 906 517 278 148
10,000 4,899 965 370 148
15,000 5,856 997 375 149
30,000 7,276 1,031 380 150
100,000 8,762 1,056 383 150
500,000 9,423 1,065 384 151
1,000,000 9,512 1,066 384 151


Table III - Valid research sample sizes; 99% confidence

Valid Sample Sizes

Confidence: 99%
Error margin: 1% 3% 5% 8%
Population Sample size
100 99 95 87 73
500 485 394 285 171
1,000 943 649 399 206
10,000 6,239 1,557 622 253
15,000 7,878 1,642 636 255
30,000 10,682 1,737 650 257
100,000 14,227 1,810 659 259
500,000 16,055 1,837 663 260
1,000,000 16,317 1,840 663 260


Here’s a breakdown of the key elements of valid research:

  • Sample size: The number of people you need to respond to your survey for it to be valid.
  • Population: The number of people in the group your Sample represents. For example, we recently calculated there are 35,000 Microsoft freelance contractors in the UK – so our Population is 35,000.
  • Confidence level: This tells you how sure you can be the results are repeatable. You have a choice of 90%, 95% or 99% confidence. Most market research is based on 95%.
  • Error margin: The plus or minus figure. An error margin of 5 on a result of 75%, means that between 70% and 80% of the population would choose the same answer.

You can see from the Tables that as the Population figure rises above 15,000 people, so the additional number of respondents needed for your research to be valid narrows. So if you are not sure of the Population size of your audience just choose 15,000.

As B2B marketers attempt to attract qualified prospects to their organisation, and opt-in becomes essential, so we will see further growth in content marketing. To rise above the noise, you should use valid research and comment on the results from a position of authority.

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