Compute t-statistic (TScore
)¶
- class cerebstats.stat_scores.tScore.TScore(*args: Any, **kwargs: Any)¶
Compute t-statistic as the standardized statistic as
Definitions
Interpretation
sample_mean, \(\bar{x}\)
observation[“mean”]
null_value, \(\mu_0\)
model prediction
standard_error, se
observation[“standard_error”]
t-statistic, t
t = \(\frac{\bar{x} - \mu_0}{se}\)
Note: se = \(\frac{s}{\sqrt{n}}\), where n is the sample size and s is the standard deviation.
Use Case
x = TScore.compute( observation, prediction ) score = TScore(x)
Note: As part of the SciUnit framework this custom
TScore
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