[Eeglablist] SIFT resampling surrogate distributions with 1 trial

[Tim Mullen]

Winslow, Makoto,

As a statistical principle, bootstrapping can only be used when you have
multiple independent and identically distributed (i.i.d) observations
available. The observations are resampled with replacement from the
original set to construct an empirical probability distribution.

It is not possible to use bootstrapping to test for statistical differences
between only two observations (i.e. two trials). In general, with any test,
your statistical power will be extremely low if you have only one
observation per condition.

You can try to mitigate this by segmenting your long continuous trials into
short 'pseudo-trials' and then testing for differences in the pseudo-trial
between conditions. Make sure that you average your causal measure over
time within each trial before computing your stats. One concern is that the
pseudotrials may be far from i.i.d within a condition, so if using
bootstrap, your bootstrap distribution may not converge to the true
distribution of the estimator and your stats will be biased.

Depending on your specific null hypothesis and protocol, however, there may
be alternative parametric and nonparametric tests you can apply.

Otherwise try to collect data for more subjects (then you simply bootstrap
across subjects e.g. using statcond with SIFT matrices) or more trials (run
your experiment more than once per condition).

Tim

On Aug 18, 2016 11:09 AM, "Makoto Miyakoshi" <mmiyakoshi at ucsd.edu> wrote:

> Dear Winslow,
>
> Yes, unfortunately the bootstrap seems to be designed for across trials.
>
> Makoto
>
> On Sat, Aug 13, 2016 at 4:57 PM, Winslow Strong <winslow.strong at gmail.com>
> wrote:
>
>> I'd like to use a resampling technique (e.g. bootstrap) to get p-values
>> and test stats for SIFT connectivity metrics for 1 subject across n
>> conditions.
>>
>> This is a steady-state condition study, hence there's only 1 trial per
>> condition.  I'm trying to analyze whether certain connectivity metrics
>> (i.e. their averages over a condition) are statistically significantly
>> different across the conditions.  I was under the impression I could use
>> SIFT's surrogate distribution generator to obtain the surrogate
>> distribution for these calculations, but when I run that from the GUI for
>> bootstrap, I get the error:
>>
>> "Unable to compute bootstrap distributions for a single trial"
>>
>> Is this surrogate function only designed to do boostrapping over trials?
>> Or is there a way to do it over windows within a condition?
>>
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>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
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