Sunday morning is a good time to pause and ask: what did we actually learn this week? Since Monday, 19 October, we’ve unpacked numbers from seven different angles: summarizing variables, environmental crime, choosing a statistical test, police posts, prediction models, crimes against life, questionnaires, and a checklist for reading crime figures. In the last two days, we also opened a new series on common misunderstandings of official statistics, plus a Q&A session.
If we squeeze it all down, there are seven lessons that I think are most worth taking home. Not the formulas, but the thinking habits.
1. More variables doesn’t mean more information
On Monday we looked at twenty-five variables that actually overlapped with each other. Factor analysis and PCA helped condense them into a few dimensions without losing the story. The lesson: before adding variables, ask whether they really measure different things. Summarizing well is often more useful than collecting more.
2. Enforcement figures measure system activity
Environmental crime was recorded at 4,149 incidents in 2024, and about 32% were concentrated in a single province. Concluding that this province is “the most damaged” is too quick. Crimes of this kind are only recorded when officers find them and act on them, so the figure also reflects how active enforcement is there. The lesson: distinguish between what happens and what gets found.
3. Method follows the question, not the other way around
On Tuesday, five questions guided us in choosing a statistical test: what the analysis is for, what the measurement scale is, how many groups there are and whether they’re paired, whether the assumptions hold, and how large the sample is. Students who pick a method first and then look for a question almost always end up rewriting their results chapter.
4. Access isn’t the only barrier
About 89.14% of villages and urban wards have no police post, yet around 85% say access is easy, and only about 20% of victims report. If physical access isn’t the main barrier, perhaps what needs building is trust. The lesson: don’t confuse the availability of a facility with its use.
5. High accuracy can mislead
A model predicting employee resignations with 85% accuracy sounds impressive, until we realize that if only 15% of employees resign, simply guessing “no one resigns” is already 85% accurate, with 0% recall. The lesson: always ask for complementary measures (precision and recall) and compare against a simple baseline.
6. Big numbers and small numbers both need context
Murder cases were recorded at 1,106 in 2024, down from 1,129. With numbers this small, minor fluctuations look large in percentage terms. Meanwhile, incidents of negligence causing death fell from 875 to 563. The lesson: for categories with small counts, be careful about drawing trends from a single year.
7. Data quality starts with the question
On Thursday we looked at seven questionnaire traps: double-barreled questions, leading questions, vague terms, absolute words, overlapping answer options, order effects, and questionnaires that are too long. No analysis, however sophisticated, can rescue a bad questionnaire. The lesson: the quality of an analysis is decided upstream, when the questions are written.
This week at a glance
| Day | Topic | Lesson |
|---|---|---|
| Monday | Factor analysis and PCA | Condense overlapping variables |
| Monday | Environmental crime | Enforcement statistics measure system activity |
| Tuesday | Choosing a statistical test | Five questions before choosing a tool |
| Tuesday | Police posts and reporting | Access isn’t the only barrier |
| Wednesday | Resignation prediction model | Accuracy alone isn’t enough |
| Wednesday | Crimes against life | Small numbers need extra care |
| Thursday | Questionnaires and checklist | Data quality starts with the question |
| Friday and Saturday | Official statistics and Q&A | The numbers are right; the reading is often wrong |
How to use these seven lessons at work
Lessons are only useful when they turn into habits. Here are a few simple ways to start next week.
In meetings: before accepting a number from a presentation, ask one clarifying question, such as “what is this calculated from?” or “what’s the comparison?”. Don’t make it rhetorical; ask out of curiosity, and presenters are usually glad, because it shows their numbers are being taken seriously.
In reports: add one line under every important number stating its source, period, and limitations. The habit takes two minutes but prevents weeks of misreading.
In research: write your list of assumptions and limitations before you write your conclusions, not after. Conclusions written after facing the limitations honestly are almost always sturdier.
At home and on social media: before sharing a surprising chart or number, stop for ten seconds and look for the original source. Most viral numbers aren’t wrong; they just lose their context along the way.
A note on sources and how to read this recap
The crime figures in this recap come from BPS Crime Statistics 2024 and were checked against the publication when each original article was written. The 85% model accuracy, salary, and questionnaire examples are illustrations to explain concepts, not data from real cases or specific clients. If you quote any number from this recap, take it from the original source and read its definition, exactly as we discussed.
What we’ll open next week
Next week the theme shifts to young people and the world of work, ahead of Youth Pledge Day on 28 October. We’ll look at how youth unemployment compares with the national figure, how unemployment breaks down by education level, and how popular claims about a whole generation can be tested with data. Midweek, we’ll also tackle an important methods question: why social media polls and random surveys can produce very different numbers.
As always, we start from the official numbers, then from the questions. If there’s a figure about young people or jobs that confuses you, send it over. I’ll add it to the discussion.
Five reflection questions for the weekend
- Which number did you quote most often this week, and do you know how it’s calculated?
- Is there a decision at your workplace that was made from a single average without looking at the spread?
- Who doesn’t show up in the data you use?
- Which analysis method did you pick because “that’s how it’s usually done,” rather than because it fits the question?
- If your number is wrong, who would be hurt most, and how could you find out earlier?
If you can only take one lesson
If I had to pick just one, my choice is the same as in previous weeks: before you believe a number, ask what was counted and what was left out. The seven lessons above are just variations of that question, applied to different data.
Tomorrow we start a new week with a topic close to many of us: young people and work, ahead of Youth Pledge Day. Same pattern: official numbers first, then questions.
Of the seven lessons above, which feels most relevant to your work or research? Share it in the comments. I always enjoy reading examples from the field.
Read also: need help analyzing and interpreting your research or business data? See our data analysis and statistical services.
Want this way of reading data applied in your team, or need help with the analysis for your research? The datalytics.indo team helps with everything from research and questionnaire design, analysis (regression, PLS-SEM/SEM, path analysis, factor, cluster, and more) and dashboards, to interpretation. Private mentoring and in-house training are also available. Let’s discuss your needs first. We’ll provide a tailored quote based on your project.
Tinggalkan Balasan