flynn002

Many people in my Twitter timeline have been calling for another referendum on Brexit. Since most of the people I follow regard Brexit as an unmitigated disaster, one can see they are desperate to adopt any measure that might stop it. Things have now got even more interesting with arch-Brexiteer, Nigel Farage, calling yesterday for another referendum. Unless he is playing a particularly complicated game, he presumably also thinks that his side will win – and with an increased majority that will ensure that Brexit is not disrupted. Let me be clear. I think Brexit is a disaster. But I really do not think another referendum is a good idea. If there's one thing that the last referendum demonstrated, it is that this is a terrible method for making political decisions on complicated issues. I'm well-educated and well-read, yet at the time of the referendum, I understood very little about how the EU worked. My main information came from newspapers and social media – including articles...

Intuitive explanations of statistical concepts for novices #4 The p-value is widely used but widely misunderstood. I'll demonstrate this in the context of intervention studies. The key question is how confident can we be that an apparently beneficial effect of treatment reflects a change due to the intervention, rather than arising just through the play of chance. The p-value gives one way of deciding that. There are other approaches, including those based on Bayesian statistics, which are preferred by many statisticians. But I will focus here on the traditional null hypothesis significance testing (NHST) approach, which dominates statistical reporting in many areas of science, and which uses p-values. As illustrated in my previous blogpost , where our measures include random noise, the distorting effects of chance mean that we can never be certain whether or not a particular pattern of data reflects a real difference between groups. However, we can compute the probability that the...

Intuitive explanations of statistical concepts for novices #3 I'll be focusing here on the kinds of stats needed if you conduct an intervention study. Suppose we measured the number of words children could define on a 20-word vocabulary task. Words were selected so that at the start of training, none of the children knew any of them. At the end of 3 months of training, every child in the vocabulary training group (B) knew four words, whereas those in a control group (A) knew three words. If we had 10 children per group, the plot of final scores would look like Figure 1 panel 1. Figure 1. Fictional data to demonstrate concept of random error (noise) In practice, intervention data never look like this. There is always unexplained variation in intervention outcomes, and real results look more like panel 2 or panel 3. That is, in each group, some children learn more than average and some less than average. Such fluctuations can reflect numerous sources of uncontrolled variation: for in...

Over a hotel breakfast at an unfeasibly early hour (I'm a clock mutant) I saw two things on Twitter that appeared totally unrelated but which captured my interest for similar reasons. The two topics were the phonics wars and the reproducibility crisis. For those of you who don't work on children's reading, the idea of phonics wars may seem weid. But sadly, there we have it: those in charge of the education of young minds locked in battle over how to teach children to read. Andrew Old (@oldandrewuk), an exasperated teacher, sounded off this week about 'phonics denialists', who are vehemently opposed to phonics instrution, despite a mountain of evidence indicating this is an important aspect of teaching children to read. He analysed three particular arguments used to defend an anti-phonics stance. I won't summarise the whole piece, as you can read what Andrew says in his blogpost . Rather, I just want to note one of the points that struck a chord with me. It'...

Intuitive explanations of statistical concepts for novices #2 In my last post , I gave a brief explainer of what the term 'Analysis of variance' actually means – essentially you are comparing how much variation in a measure is associated with a group effect and how much with within-group variation. The use of t-tests and ANOVA by psychologists is something of a historical artefact. These methods have been taught to generations of researchers in their basic statistics training, and they do the business for many basic experimental designs. Many statisticians, however, prefer variants of regression analysis. The point of this post is to explain that, if you are just comparing two groups, all three methods – ANOVA, t-test and linear regression – are equivalent. None of this is new but it is often confusing to beginners. Anyone learning basic statistics probably started out with the t-test. This is a simple way of comparing the means of two groups, and, just like ANOVA, it looks at...

Intuitive explanations of statistical concepts for novices #1 Lots of people use Analysis of Variance (Anova) without really understanding how it works, so I thought I'd have a go at explaining the basics in an intuitive fashion. Consider three experiments, A, B and C, each of which compares the impact of an intervention on an outcome measure. The three experiments each have 20 people in a control group and 20 in an intervention group. Figure 1 shows the individual scores on an outcome measure for the two groups as blobs, and the mean score for each group as a dotted black line. Figure 1: Simulated data from 3 intervention studies In terms of average scores of control and intervention groups, the three groups look very similar, with the intervention group about .4 to .5 points higher than the control group. But we can't interpret this difference without having an idea of how variable scores are in the two groups. For experiment A, there is considerable variation within each gro...

There's been some interesting discussion on Twitter about the high rate of developmental language disorder (DLD) in the prison population. Some studies give an estimate as high as 50 percent (Anderson et al, 2016), and this has prompted calls for speech-language therapy services to be involved in the working with offenders. Work by Pam Snow and others has documented the difficulties of navigating the justice system if your understanding and ability to express yourself are limited. This is important work, but I have worried from time to time about the potential for misunderstanding. In particular, if you are a parent of a child with DLD, should you be alarmed at the prospect that your offspring will be incarcerated? So I wanted to give a brief explainer that offers some reassurance. The simplest way to explain it is to think about gender. I've been delving into the latest national statistics for this post, and found that the UK prison population this year contained 82,314 men,...