The weather forecast for the weekend said there is a 90% chance of rain. But it didn’t rain at all.
Does that mean the forecast was wrong?
Not really. All forecast meant was that the odds of it raining are 9 in 10.
Statistics can easily mislead. In the above example, it’s both easy and common to overlook that there were odds of 1 in 10 (a 10% probability) that it may not rain at all.
The government announced the current unemployment in Australia is 7.1 %. The definition of unemployed requires someone to be “actively seeking a job” and doesn’t take into account people on social welfare. Is the unemployment rate truly 7.1% then?
While the actual numbers don’t lie, they can in fact be used to mislead. Sometimes purposefully, sometimes not.
The organisations across the globe are hyper-focused on gathering and analysing massive amounts of data. What’s often missing is the contextualisation of data.
Do you understand those trends correctly? Do they actually represent the true business behaviour? What is your underlying assumption or bias when looking at that data?
Remember the data and statistics are only as good as the person interpreting it.