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Data management

Excel is great for organizing your data and making simple calculations (e.g., means / standard deviations), but it is pretty worthless for statistics, and even more so for graphs. You can apparently get some plugins that will allow Excel to do simple stats (although I haven't tried it).

You will probably be familiar with SPSS for running basic factorial ANOVAs, correlations, etc. from your stats classes. SPSS is OK, but I like Statistica more.... Its Statistica Electronic Manual is an excellent general guide to statistics, and its statistical advisor function is very helpful for determining how to analyze your specific data set. Statistica 13 (newest version) is available for free with an @llu.edu email address at llu.onthehub.com.

GraphPad Prism seems to be the easiest / best current scientific graphing program, but it’s co$tly. However, there are plenty of cheaper and/or free (although probably less user-friendly) programs for Mac, Windows, or Linux. If you are going to be making graphs for years to come, Prism is probably worth it. That being said, there is an older copy installed on the computer in the meeting area of my office that everyone is able to access and use.

So, my current suggestions for getting your work done most easily and efficiently with the cheapest and/or best tools are to:

  • Maintain all lab notebook entries / background info / knowledge base with your lab notebook.
  • Collect / organize your pdfs / publications with a dedicated reference manager (Zotero, Mendeley, Endnote, etc.)
  • Determine required sample sizes with G*Power.
  • Collect / organize your data with Microsoft Excel.
  • Analyze your data using R, Statistica, SPSS, etc.
  • Make your graphs with Prism (or R, or any number of free graphing packages)
  • Organize presentations and figures for publications with Microsoft Powerpoint.
  • Write up your methods / results etc. with Microsoft Word.


That all being said, we are in the transition stage of moving data organization / analysis from Excel, Statistica and Prism to Python and R, which are open source scientific programming languages. Stay tuned.

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