Exploring Data

We looked at 3 example astronomical datasets:

  • An image from XMM_Newton

  • An object catalog from SDSS

  • A set of measurements from Riess et al 2011.

In each case, it was useful to:

  • Visualize the data in various ways

  • Summarize the data, to reduce its dimensionality and bring out interesting structure

Classical statistics, such as

  • Means, medians and variances

  • Histograms

  • Correlation functions

  • and so on

are not only useful tools for summarizing data:

they get passed forward to new analyses where they are treated as data themselves.

One answer to our question "What is Data?" is therefore:

Data are *constants* 
  (usually numbers) 
    that we are *handed* 
      (typically in a data file) 
        that *we hope to learn something from.*

In the next session we'll look at how learning from data - inference - works.


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