Note

Click here to download the full example code

# Discriminability TestingΒΆ

If you have repeated measures from the same subject, and want to see if these are different than those from other subjects. Let's look at the mathematical formulations:

With \(D_x\) as the sample discriminability of \(x\), one sample test performs the following test:

where \(D_0\) is the discriminability that would be observed by random chance.

This can also be formulated as a two-sample test. Let \(\hat{D}_x\) denote the sample discriminability of one approach, and \(\hat{D}_y\) denote the sample discriminability of another approach. Then,

Alternative tests can be done for \(D_x < D_y\) and \(D_x \neq D_y\).

Like all the other tests within hyppo, each method has a `statistic`

and
`test`

method. The `test`

method is the one that returns the test statistic
and p-values, among other outputs, and is the one that is used most often in the
examples, tutorials, etc.
The p-value returned is calculated using a permutation test.

**Discrimnability one-sample** and
**Discrimnability two-sample** are time series tests of independence.
More details can be found in `hyppo.discrim.DiscrimOneSample`

and
`hyppo.discrim.DiscrimTwoSample`

.

Each class has a `is_dist`

parameter that indicates whether or not inputs are
distance matrices. These distances must be Euclidean distance.
Also, `remove_isolates`

indicates whether or not to remove measurements with a single
instance.
Otherwise, these tests runs like any other test.

**Total running time of the script:** ( 0 minutes 0.000 seconds)