There are a variety of free online tools that can be easily accessed to address various data analysis needs, such as the randomization tools described in the last two Newsletter issues. One would only need to be aware of such resources, maintain these references and links in a convenient format and place and, last but not least, make sure that these resources are available in the future when the need comes.

Looking for something free, good-quality, easily accessible and likely to be always there for you? There is such a tool: ‘R’, a data analysis and graphics package that is becoming increasingly popular, as it is seen by many people as a tool to establish reproducible research practices (as explained in this Coursera course as well as in many publications found online).
The graphics part of R may seem difficult to use, but writing the data analysis scripts can be quickly learned as there are numerous free examples shared by the vast R community (e.g. or You may want to read this Introduction, that contains a number of useful references, or look at this Tutorial.
After you have installed R (using ‘R Studio’ to make communication with R easier), you will be able to ask Google or the R community (see the links above) a question of interest (e.g. how does one do ANOVA in R?) to get clear instructions.
R has specialized packages for a variety of specific tasks. For Block Randomization, a special R package is available (it takes less than a minute to install) and a complete manual can be found here:
If you find it difficult and need more time to learn, we are certain your colleagues at the biostatistics departments are skilled in R programming and will be happy to provide you with a ready-to-use script.
Try R!