The questions in Dear ACME Mwalimu come from the hundreds of real messages and emails sent to our training desk every month. No question deserves just one answer, so we are answering them here where everyone can benefit. Names and details have been removed, and permission to publish the questions has been sought. If you have a question for ACME Mwalimu, send it to training[at]acme-ug.org, and it may appear in a future instalment.
Dear ACME Mwalimu,
My editor said something that puzzled me when I handed in my first draft on a story about landslides and their impact on families in my region. She told me to “question the numbers.”
I nodded, went back to my desk, and sat there not sure what I was supposed to do differently.
My draft had data from the meteorological authority, the Bureau of Statistics, the government department responsible for disaster preparedness, and an international body that tracks environmental change. I had the number of families displaced, figures on the areas most affected, and climate data on rainfall patterns and how they have changed over time. I even had historical data on previous landslides in the region. I was not just making things up or quoting one displaced family and calling it data. So when she said question the numbers, I genuinely did not know what she was seeing that I was not.
I have not asked her to explain further, because we have had this conversation twice already and I don’t want to be the person who still doesn’t understand after the third time.
What does it actually mean, in practice? I am struggling here.
Sincerely,
Numbers Don’t Have a Mouth
ACME Mwalimu responds
Dear Numbers Don’t Have a Mouth,
What you brought to that story was more than most journalists bother with. But your editor is not asking whether you found numbers. She is asking whether you interrogated them.
You already know how to do this. For instance, when a government official tells you that the landslides were an unavoidable natural disaster, you do not simply write that down. You ask how they know, who told them, and who might see it differently. Data is no different. Data are sources presenting a version of events, and they deserve the same scepticism and the same follow-up questions as any other source.
So start here. How was each number arrived at? The meteorological authority and the international environmental body may both have rainfall data for the region, but measured differently, over different time periods, using different baselines. Do their figures actually agree? If they diverge, that is worth understanding before you put both in the same story as though they are saying the same thing. The displacement figures from the disaster preparedness department are another example. They may only count households that registered at official relief centres, which means the families who went to stay with relatives or were too far from a registration point simply do not appear in that number. That gap between who was counted and who was actually affected is part of your story.
Then go wider. Every number sits inside a dataset, a study, or a report full of other figures that give it meaning. A single figure on rainfall may tell you something, but it becomes far more useful when you put it next to data on deforestation, hillside settlement patterns, and soil conditions in the same region. Taken together, those numbers can show not just that the landslides happened, but why, and why here, and why now. The same is true of the displacement figures. A count of affected families sits next to data on where people were living, how long they had been there, and what, if anything, had been done to warn or protect them. When you read across a dataset rather than extracting from it, you move from describing what happened to understanding what it means.
A number reported on its own is just a fact. A number held up against another number, with the gap between them explained, is your story.
And then comes the hardest part: the writing. All the data you collect has to become something a reader can follow and feel. This means finding the family whose experience makes the displacement figures real. It means explaining not just that rainfall has intensified, but what that has meant for the hillside where people have been building homes for twenty years. A good data-driven story does not drown the reader in figures. It uses numbers the way a farmer uses a hoe: with purpose, with precision, and only where they are needed.
Your editor is not asking you to find different numbers. She is pushing you to do more with the numbers you already have.
Keep asking,
ACME Mwalimu
