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Current plan:

I am planning to trial sensor type B by installing three sensors monitoring the same process:

Sensor 1) Sensor type A, this is the reference sensor

Sensor 2) Sensor type A, this is candidate #1

Sensor 3) Sensor type B, this is candidate #2

Generate two error time series: one with the error between candidate #1 and reference and the second the error between candidate #2 and reference. The conclusion of a successful trial should be able to state that the differences of both errors are statistically insignificant.

Statistical test:

My first thought was to use a Student's t-test to compare the error signals. But I understand the t-test only tests for differences in mean values. But I suspect, I also need to know if the error variances are the same.

Questions:

- Will the F-test provide a test that is sensitive to both mean and variance differences?

- Would anyone suggest an alternative approach?

- I am collecting data for a 24 hour period. During this time, the process operates under 5 different regimes. Should I break-up the time series into 5 segments and run separate tests?

Will this split approach change the test criteria?

Other info:

- The error signals have a good approximation to a normal distribution

- The measurement noise is not time-correlated