The American linguist Lauren Hall-Lew, currently living in Edinburgh, was musing on Twitter recently about how both Scotch and Oriental are considered offensive when categorizing people, but not offensive when describing alcohol or rugs. Her main point is valid and very important: as I’ve discussed before, emotions can run very high when discussing how to categorize people, this is because so much more is at stake.

In a couple of recent posts I talked about the idea of speech role models for language learning, specifically on fluent, clear non-native speakers providing more accessible models for students learning after the teenage years. I ended with a caution against “cloning” a single non-native speaker, raising the specter of a class of students who all come out speaking English like Javier Bardem.

I wrote recently that if you want to be confident in generalizing observations from a sample to the entire population, your sample needs to be representative. But maybe you’re skeptical. You might have noticed that a lot of people don’t pay much attention to representativeness, and somehow there are hardly any consequences for them. But that doesn’t mean that there are never consequences, for them or other people.

In a recent post, I talked about using speech role models to teach English as a Second Language (ESL). In my class at Saint John’s University I told my students to find a native English speaker that they admired and wanted to sound like, but some of the students seemed discouraged and the distance between their accents and the accents of their role models was very large. I guessed that they may have felt that the gap was insurmountable.

Recently I’ve talked about the different standards for existential and universal claims, how we can use representative samples to estimate universal claims, and how we know if our representative sample is big enough to be “statistically significant.” But I want to add a word of caution to these tests: you can’t get statistical significance without a representative sample.

John Murphy of Georgia State published an article about using non-native speakers, and specifically the Spanish actor Javier Bardem, as models for teaching English as a Second Language (ESL) or as a foreign language (EFL). Mura Nava tweeted a blog post from Robin Walker connecting Murphy’s work to similar work by Kenworthy and Jenkins, Peter Roach and others.

In my post last week I talked about the importance of representative samples for making universal statements, including averages and percentages. But how big should your sample be? You don’t need to look at everything, but you probably need to look at more than one thing. How big a sample do you need in order to be reasonably sure of your estimates? One of the pioneers in this area was a mysterious scholar known only to the public as Student.

My dissertation focused on the evolution of negation in French, and I’ve continued to study this change. In order to track the way that negation was used, I needed to collect a corpus of texts and annotate them. I developed a MySQL database to store the annotations (and later the texts themselves) and a suite of PHP scripts to annotate the texts and store them in the database.

In my previous post, I discussed the differences between existential and universal statements. In particular, the standard of evidence is different: to be sure that an existential statement is correct we only need to see one example, but to be sure a universal is correct we have to have examined everything. But what if we don’t have the time to examine everything, and we don’t have to be absolutely sure?

There’s a famous story about swans that Nasim Taleb used for the title of his recent book. European zoologists had seen swans, and all the swans they had seen had white feathers, so they said that all the swans in the world were in fact white. Then a European went to Australia and saw swans with black feathers.