Archive for January 2009

accessing data, easier than before but…

Someone emailed me for globular cluster data sets I used in a proceeding paper, which was about how to determine the multi-modality (multiple populations) based on well known and new information criteria without binning the luminosity functions. I spent quite time to understand the data sets with suspicious numbers of globular cluster populations. On the other hand, obtaining globular cluster data sets was easy because of available data archives such as VizieR. Most data sets in charts/tables, I acquire those data from VizieR. In order to understand science behind those data sets, I check ADS. Well, actually it happens the other way around: check scientific background first to assess whether there is room for statistics, then search for available data sets. Continue reading ‘accessing data, easier than before but…’ »

Likelihood Ratio Technique

I wonder what Fisher, Neyman, and Pearson would say if they see “Technique” after “Likelihood Ratio” instead of “Test.” A presenter’s saying “Likelihood Ratio Technique” for source identification, I couldn’t resist checking it out not to offend founding fathers of the likelihood principle in statistics since “Technique” sounded derogatory to be attached with “Likelihood” to my ears. I thank, above all, the speaker who kindly gave me the reference about this likelihood ratio technique. Continue reading ‘Likelihood Ratio Technique’ »

Lost in Translation: Measurement Error

You would think that something like “measurement error” is a well-defined concept, and everyone knows what it means. Not so. I have so far counted at least 3 different interpretations of what it means.

Suppose you have measurements X={Xi, i=1..N} of a quantity whose true value is, say, X0. One can then compute the mean and standard deviation of the measurements, E(X) and σX. One can also infer the value of a parameter θ(X), derive the posterior probability density p(θ|X), and obtain confidence intervals on it.

So here are the different interpretations:

  1. Measurement error is σX, or the spread in the measurements. Astronomers tend to use the term in this manner.
  2. Measurement error is X0-E(X), or the “error made when you make the measurement”, essentially what is left over beyond mere statistical variations. This is how statisticians seem to use it, essentially the bias term. To quote David van Dyk

    For us it is just English. If your measurement is different from the real value. So this is not the Poisson variability of the source for effects or ARF, RMF, etc. It would disappear if you had a perfect measuring device (e.g., telescope).

  3. Measurement error is the width of p(θ|X), i.e., the measurement error of the first type propagated through the analysis. Astronomers use this too to refer to measurement error.

Who am I to say which is right? But be aware of who you may be speaking with and be sure to clarify what you mean when you use the term!


The year 2009 is the Darwin bicentennial and the sesquicentennial of the publication of the Origin of Species, but, um, even more importantly, it is the International Year of Astronomy, celebrating 400 orbits since Galileo started to look through a telescope.