Publishing Partner: Cambridge University Press CUP Extra Publisher Login
amazon logo
More Info


New from Oxford University Press!

ad

Speaking American: A History of English in the United States

By Richard W. Bailey

"Takes a novel approach to the history of American English by focusing on hotbeds of linguistic activity throughout American history."


New from Cambridge University Press!

ad

Language, Literacy, and Technology

By Richard Kern

"In this book, Richard Kern explores how technology matters to language and the ways in which we use it. Kern reveals how material, social and individual resources interact in the design of textual meaning, and how that interaction plays out across contexts of communication, different situations of technological mediation, and different moments in time."


Academic Paper


Title: Our statistical intuitions may be misleading us: Why we need robust statistics
Author: Jenifer Larson-Hall
Email: click here TO access email
Institution: Kyushu University
Linguistic Field: Discipline of Linguistics; Language Acquisition
Abstract: Most academics' intuitions about statistics follow those of naive laypeople – that is, we often think that a sample should reflect the population characteristics more closely than it does, and expect less variability in samples than is truly found in them. These intuitions may prevent us from understanding why modern developments in statistics are needed. Another intuition most researchers hold is that it is better to be conservative when performing statistics, and this may involve adjusting p-values for multiple tests, using more conservative post hoc tests, or setting an alpha value lower than .05 when possible. However, the more we try to control against making an error in being overeager to find differences, the stronger the probability that we will make an error in not finding differences that actually exist. These two forces need to be counterbalanced, and this involves increasing the power of our tests. Robust statistics can increase the power of statistical tests to find real differences. I discuss the need for robust techniques to avoid reliance on classical assumptions about the data. Examples of robust analyses with t-tests, correlation, and one-way ANOVA are shown.

CUP AT LINGUIST

This article appears IN Language Teaching Vol. 45, Issue 4, which you can READ on Cambridge's site or on LINGUIST .



Add a new paper
Return to Academic Papers main page
Return to Directory of Linguists main page