# causation

16 December 2013

## Deterministic and Probabilistic models and thinking

The way we understand and make sense of variation in the world affects decisions we make. Part of understanding variation is understanding the difference between deterministic and probabilistic (stochastic) models. The NZ curriculum specifies the following learning outcome: “Selects and uses appropriate methods to investigate probability situations including experiments, simulations, and theoretical probability, distinguishing between deterministic and probabilistic models.” This is at level 8 of the curriculum, the highest level of secondary schooling. Deterministic and probabilistic models are not familiar to all teachers of mathematics and statistics, so I’m writing about it today. Model The term, model, is itself challenging. […]
21 October 2013

## Proving causation

Aeroplanes cause hot weather In Christchurch we have a weather phenomenon known as the “Nor-wester”, which is a warm dry wind, preceding a cold southerly change. When the wind is from this direction, aeroplanes make their approach to the airport over the city. Our university is close to the airport in the direct flightpath, so we are very aware of the planes. A new colleague from South Africa drew the amusing conclusion that the unusual heat of the day was caused by all the planes flying overhead. Statistics experts and educators spend a lot of time refuting claims of causation. […]
28 January 2013

## Make journalists learn statistics

All journalists should be required to pass a course in basic statistics before they are let loose on the unsuspecting public. I am not talking about the kind of statistics course that mathematical statisticians are talking about. This does not involve calculus, R or anything tricky requiring a post-graduate degree. I am talking about a statistics course for citizens. And journalists. 🙂 I have thought about this for some years. My father was a journalist, and fairly innumerate unless there was a dollar sign involved. But he was of the old school, who worked their way up the ranks. These […]
26 June 2012

## All models are wrong

In my title I quote George Box, who wrote,  “Essentially, all models are wrong, but some are useful“. I wish economists would remember this more often. Statistics and Operations Research (and many other sciences) are based on the concept of a mathematical model. Aspects of a “real world” problem are quantified, analysed, explored, experimented with, sometimes even optimised, and the results are linked back to the original problem. This idea of a model is one we have tried to teach our students. It is a surprisingly difficult idea, and one that needs frequent revisiting. The following diagram is more complex […]
22 May 2012

## Significance

In statistical analysis the word “significant” means that there is evidence that effect found in the sample exists in the population from which the sample was drawn. The choice of the word “significant” is unfortunate, as it is used to mean something different in common language. Reporters hear a scientist say that there is a significant effect, and tend to think big. Results gets reported as significant, meaning big, and we have effect inflation. In reality, if we take a large enough sample, even a small effect will show up as significant. Because the sample is large, it is easier […]
15 March 2012

## Seductive Causation

Causation is a seductive notion. We want to make meaning out of our world. I love playing “the beeping nose” with little children. I press their nose and it beeps. I press my nose and it whirrs. It fascinates them. They have discovered cause and effect. They can make cool sounds by pressing noses. You can keep them amused for quite some time. Cause and effect implies control. If we know what causes things we are better able to control them. Scientific endeavor is largely a search for causes. History is littered with examples of misplaced cause and effect theories. […]