(This article was originally published at Learn and Teach Statistics and Operations Research, and syndicated at StatsBlogs.)

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 days most media people have degrees, and I am adamant that the degree should contain basic numeracy and statistics. The course I devised (which has now been taken over by the maths and stats department and will be shut down later this year, but am I bitter…?) would have been ideal. It included basic number skills, including percentages (which are harder than you think), graphing, data, chance and evidence. It required students to understand the principles behind what they were doing rather than the mechanics.

Here is what journalists should know about statistics:

# Chance

One of the key concepts in statistics is that of variability and chance. Too often a chance event is invested with unnecessary meaning. A really good example of this is the road toll. In New Zealand the road toll over the Easter break can fluctuate between 21 (in 1971) and 3 in 1998, 2002 and 2003. Then in 2012 the toll was zero, a cause of great celebration. I was happy to see one report say “There was no one reason for the zero toll this Easter, and good fortune may have played a part.” However this was a refreshing change as normally the police seem to take the credit for good news, and blame bad news on us. Rather like Economists.

With any random process you will get variability. The human mind looks for patterns and meanings even where there are none. Sadly the human mind often finds patterns and imbues meaning erroneously. Astrology is a perfect example of this – and watching Deal or No Deal is inspiring in the meaning people can find in random variation.

All journalists should have a good grasp of the concepts of variability so they stop drawing unfounded conclusions

# Data Display

There are myriad examples of graphs in the media that are misleading, badly constructed, incorrectly specified, or just plain wrong. There was a wonderful one in the Herald Sun recently, which has had considerable publicity. We hope it was just an error, and nothing more sinister. But good subediting (what my father used to do, but I think ceased with the advent of the computer) would have picked this up.

There is a very nice website dedicated to this: StatsChat. It unfortunately misquotes H.G.Wells, but has a wonderful array of examples of good and bad statistics in the media. This post gives links to all sorts of sites with bad graphs, many of which were either produced or promulgated by journalists. But not all – scientific literature also has its culprits.

Just a little aside here – why does NO-ONE ever report the standard deviation? I was writing questions involving the normal distribution for practice by students. I am a strong follower of Cobb’s view that all data should be real, so I went looking for some interesting results I could use, with a mean and standard deviation. Heck I couldn’t even find uninteresting results! The mean and the median rule supreme, and confidence intervals are getting a little look in. Percentages are often reported with a “margin of error” (does anyone understand that?). But the standard deviation is invisible. I don’t think the standard deviation is any harder to understand than the mean. (Mainly because the mean is very hard to understand!) So why is the standard deviation not mentioned?

# Evidence

One of the main ideas in inferential statistics is that of evidence: The data is here; do we have evidence that this is an actual effect rather than caused by random variation and sampling error? In traditional statistics this is about understanding the p-value. In resampling the idea is very similar to that of a p-value – we ask “could we have got this result by chance?” You do not have to be a mathematician to grasp this idea if it is presented in an accessible way. (See my video “Understanding the p-value” for an example.)

One very exciting addition to the New Zealand curriculum are Achievement Standards at Years 12 and 13 involving reading and understanding statistical reports. I have great hopes that as teachers embrace these standards, the level of understanding in the general population will increase, and there will be less tolerance for statistically unsound conclusions.

Another source of hope for me is “The Panel”, an afternoon radio programme hosted by Jim Mora on Radio New Zealand National. Each day different guests are invited to comment on current events in a moderately erudite and often amusing way. Sometimes they even have knowledge about the topic, and usually an expert is interviewed. It is as talkback radio really could be. I think. I’ve never listened long enough to talk-back radio to really judge as it always makes me SO ANGRY! Breathe, breathe…

I digress. I have been gratified to hear people on The Panel making worthwhile comments about sample size, sampling method, bias, association and causation. (Not usually using those exact terms, but the concepts are there.) It gives me hope that critical response to pseudo-scientific, and even scientific research is possible in the general populace. My husband thinks that should be “informed populace”, but I can dream.

It is possible for journalists to understand the important ideas of statistics without a mathematically-based and alienating course. I feel an app coming on… (Or should that be a nap?)

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