Slightly off the topic, but still potentially relevant in terms of realistic experimental error: when dealing with the small volumes typically used in crystallization (say 1 uL + 1 uL drops), and using a 10 uL pipette, the errors are fairly high (more like 30% than 5-10%), leading to a lot of non-reproducibility in the experiment- even when setting up the same exact solution many times. Going to robotics helps with the reproducibility in liquid transfer, but doesn't necessarily help with the reproducibility of crystallization (an example of this can be found in: http://journals.iucr.org/d/issues/2007/07/00/bw5202/ ).
Cheers, tom From: CCP4 bulletin board [mailto:[email protected]] On Behalf Of [email protected] Sent: Thursday, 14 March 2013 12:07 PM To: [email protected] Subject: Re: [ccp4bb] statistical or systematic? bias or noise? I think that in statistics you can build a model that describes (and predicts) the uncertainty. So if you have done similar (!) replicate experiments, from which you can build the model, you can apply it to a single observation and provide a reasonably good guess for the value that you were measuring and its variance. Of course that guess would not be as good as the average value and variance from true replicates. With protein crystals (or solutions for that matter), the sample is often too precious to redo the experiment and it is worth thinking about doing replicate experiments with a "cheap one", build the model, and then apply it to single "expensive" observations. That would be statistically justified (provided that the model is valid for all sets of experiments). I have not built such models, but we know that pipetting isn't really as good as we believe. If you randomly dial to a particular value on your pipetteman (say 5 uL), you will get a certain pattern of "errors" (which is really not a good word for it), while if you consistently dial either from a low (1uL) or a high (10uL) value towards the value you want, you will get another pattern. Those two patterns are not representative of each other, I don't think, and you would need to understand how to do experiments consistently to stay within your error-model (bad word). Among many other things, statisticians try to come up with models that explain the uncertainty so that you know what to think, even if your set of observation is too small to say for sure, with n=1 being the ultimate too small. (Maybe not ultimate, n=0 is really too small.) Mark -----Original Message----- From: Alexander Aleshin <[email protected]<mailto:[email protected]>> To: CCP4BB <[email protected]<mailto:[email protected]>> Sent: Wed, Mar 13, 2013 3:05 pm Subject: Re: [ccp4bb] statistical or systematic? bias or noise? On Mar 13, 2013, at 1:36 PM, Ed Pozharski wrote: But what if I only have one measurement worth of sample? Is it proper to use statistical analysis for a single measurement? I thought statistics, by definition, means multiple measurements. Alex
