Compute z-statistic (ZScoreStandard)

class cerebunit.statistics.stat_scores.zScore.ZScoreStandard(*args, **kwargs)

Compute t-statistic as the standardized statistic as

Definitions Interpretation
sample_mean, \(\bar{x}\) observation[“mean”]
null_value, \(\mu_0\) model prediction
standard_deviation, sd observation[“standard_deviation”]
z-statistic, z z = \(\frac{\bar{x} - \mu_0}{sd}\)

Note: se = \(\frac{s}{\sqrt{n}}\), where n is the sample size and s is the standard deviation.

Use Case

x = ZScoreStandard.compute( observation, prediction )
score = ZScoreStandard(x)

Note: As part of the SciUnit framework this custom ZScoreStandard should have the following methods,

  • compute() (class method)
  • sort_key() (property)
  • __str__()
classmethod compute(observation, prediction)

Note:

  • observation (sample) is in dictionary form with keys mean and
  • standard_error whose value has magnitude and python quantity
  • the populations parameter is the predicted value