Statistics:
Hated and Loved

Why people react so differently

Ask people what they think about statistics and you will hear very different answers. Some think of competition, production, progress, and facts. Others think of surveillance, punishment, and control. The numbers may look similar, but the experiences behind those reactions are very different.

In sports, statistics are part of the entertainment. Baseball fans study the figures on stadium screens and television broadcasts. Team managers use them to make decisions, while spectators use them to debate who should play and why.

At work, the same enthusiasm cannot be taken for granted. After the wall between East and West Germany came down, I encountered workers from the East who strongly rejected performance measurement. In conversations about their reaction, some told us that they associated statistics with government control and falsified reports.

Their objection made sense in the light of their experience. Before asking people to trust a measurement system, we need to understand what measurement has meant in their lives.

Start with the motive

Why does a company or government collect statistics? What happens when the numbers fall? The answers determine whether people experience measurement as support or as a threat.

Some of the East German workers described production quotas under five-year plans that they could not realistically meet. Their explanation was not that they disliked working. They lacked the resources to do the work.

One example stayed with me: a harvesting machine could break down, but obtaining a replacement part might take so long that the crop was lost before the repair could be completed. The quota remained. According to the accounts shared with us, reporting false numbers became a way of coping with demands that reality made impossible.

I present these as recollections of conversations, rather than a comprehensive account of life in East Germany. They illustrate a practical problem: when people are punished for circumstances they cannot change, the pressure to produce acceptable reports can become stronger than the incentive to report accurately.

The useful response would have been to identify the constraint and repair the supply process. A higher target cannot replace a missing spare part.

When measurement became useful

I saw a different outcome when we introduced performance statistics in an auto-parts warehouse. Initially, the workers were skeptical. They did not see how another set of numbers would benefit them.

Over the following three months, their attitude changed. When a graph went down, management investigated the bottleneck and helped correct it. A poor result prompted assistance rather than punishment. The figures made the work visible, including the small interruptions and workflow problems that had previously been easy to overlook.

The workers began to see that a weekly review could improve their daily work, whether the graph had risen or fallen. They became involved in finding solutions and reorganizing their workplace. The statistics meeting became something they looked forward to.

In the results I recorded at the time, measured warehouse performance tripled over six months. The workplace became cleaner and better organized. Fast-moving products were placed closer to the packing area, reducing unnecessary travel. The latest inventory count reported 99.4% accuracy.

Those figures describe this particular implementation. The important lesson for me was the change in ownership: the workers began shaping the system themselves because they could see how it helped.

Measure useful work

One of the first questions in any measurement project is: “What should we count?”

The answers often begin with errors, incorrect shipments, quality problems, and absences. These can be important signals. But a system that shows only failures gives an incomplete picture of the work and can turn every review into an uncomfortable search for someone to blame.

My starting point is the valuable product or service being delivered. In a warehouse, that might be a correctly picked part, a properly packed box, a correctly processed shipment, a dispatched pallet, or an accurately received delivery.

Each represents something completed that has value for the next person in the process. Measuring completed work gives people a clear view of what they are achieving.

In the warehouse example, workers recorded more than twenty different activities. These contributed to an overall performance measure. When activities differ in effort or complexity, their weighting needs to be clear; simply adding unlike tasks can give a misleading result.

Output also needs context. Speed alone is not success if accuracy declines, orders become more difficult, or more people are required to do the work. Completed work, quality, workload, and available resources should be considered together.

Workers entered their own figures. That gave them ownership, but it also made honesty essential. At the time, an inaccurate tally could cost a worker the day’s points, and the group reinforced the rules. Looking back, I would distinguish deliberate falsification from an ordinary recording mistake. A useful system needs a fair way to correct errors without making people afraid to report them.

Why I prefer graphs

Numbers give us precision. Graphs help us see movement.

A well-designed graph lets us recognize a rising trend, a sudden drop, or a recurring pattern more quickly than a column of figures. It also makes it easier to compare current performance with earlier periods.

I think of it a little like music in a film. A melody can communicate mood before anyone speaks. A graph can communicate the shape of a situation before we examine every number.

That first impression still needs checking. A graph should identify what is measured, the units, the time period, and the scale. A dramatic-looking change may come from a compressed axis, and a smooth trend may hide important differences. The picture should invite investigation and make the figures easier to understand.

Compare carefully—and ask what changed

Statistics become useful when comparisons help us decide what to investigate. That requires comparable definitions, time periods, and conditions.

Consider a business with sales of $9 million in May and $8 million in June. The previous June produced $7.5 million. June sales have fallen by approximately 11.1% from May, while increasing by approximately 6.7% from the same month a year earlier. Both statements are true.

A manager who looks only at the year-over-year increase could miss a developing problem. A manager who looks only at the monthly decline could mistake an ordinary seasonal pattern for a failure. Neither comparison answers the question on its own.

I would want to examine both, then ask what changed. Was there a new price list? Were there fewer selling days? Did product availability change? Did a new salesperson alter an effective procedure? Does the same seasonal pattern appear across several years?

A falling graph is a reason to investigate, not proof of a particular cause. Likewise, a rising graph does not mean there is nothing left to improve.

Seasonality is real, but it can also be a planning challenge. A business may be able to develop a complementary product or find customers whose needs peak at a different time.

One example I described in the original essay was a family operating fishing cabins beside a Canadian lake. Summer demand was strong, but winter access was difficult and the lake froze. A second fishing operation in the southern United States served a different season. The idea was to build a business around complementary demand rather than simply accept a long idle period.

Put the information where people can use it

A measurement system belongs close to the decisions it supports. Warehouse workers need a clear view of their own activity. Managers who travel need access wherever they work.

In the original essay, I argued for an online system rather than spreadsheets passed back and forth by email. The underlying goal remains the same: one reliable source, accessible to the right people, with a clear view of both current results and historical trends.

Access should reflect responsibilities. People should be able to see the information needed for their work without exposing confidential records unnecessarily. Secure authentication, appropriate permissions, and protection of the data are part of the design. Being online or password-protected alone does not make a system secure.

Entering figures should be straightforward. Where reliable data already exists in accounting, sales, or other operational systems, bringing it into the measurement system automatically can reduce duplicate work and transcription errors. The definitions and sources still need to be checked.

The dashboard should put the most useful information first. More charts do not automatically create more understanding. The purpose is to help someone notice a meaningful change and decide what to do next.

The result that mattered

After the warehouse points system had been introduced, the warehouse manager sent an encouraging report. He described a more active team, more initiative around the warehouse, and a stronger focus on getting every order out. In his view, the tracking had produced a noticeable increase in output without becoming an extra burden.

That response captured what I wanted the system to accomplish. Measurement had become part of making the work better.

For me, the test of a statistics system is what people do with it. Does it reveal a problem early enough to help? Does it lead to better resources, clearer decisions, and useful improvements? Can people report an unwelcome result honestly?

When the answers are yes, statistics can become something people value. Trust grows when the numbers lead to help—and when the people doing the work can see the difference.