The phrase "a true distribution" is correct and usable in written English.
It can be used in contexts related to statistics, probability, or data analysis when discussing the characteristics of a distribution that accurately represents a dataset or phenomenon.
Example: "In order to validate our model, we need to compare it against a true distribution of the observed data."
Alternatives: "an accurate distribution" or "a genuine distribution".
This function is sometimes called "information gain," albeit it measures an information loss from using a "test" distribution to approximate a "true" distribution.
However, it is necessary to determine in other model plants, such as rice, whether enrichment of these E3 ligase families was coincidental or reflected a true distribution pattern.
Here, we corroborate the contention that a bimodal distribution of scores is an artefact of human interpretation of subtly different images rather than a true distribution.
In addition, all investigated inflammatory mediators were found in the basolateral region of the acinar cell, whereas α-amylase was found mostly in the apical region, indicating a true distribution within acinar cells (representative shown for TNF α/ α-amylase).
Baseline costs for RCT populations may therefore be different from populations in claims databases or community-based studies in that the population sample is not likely to reflect a true distribution of the severity of comorbid diseases.
Ideally, one would like to compare the result of the MALDI imaging experiment against a known "true" distribution of a measured protein.
Since a true quantitative assessment of the normalization seems therefore not feasible, we have chosen a slightly simpler approach: We do not compare the result of the normalization against an unknown "true" distribution, but against the non-normalized result and the histology for comparison.
Bearing in mind that the forest cover map used in this methodology functions as a proxy for the true distribution of forest, it is important to choose a map which best serves analytical needs.
The premise of Bootstrapping is that, in the absence of the true distribution, a conclusion about the distribution can be drawn from the samples obtained.
Since this happens in a controlled manner the true distribution can be obtained afterward, as frequently used in variance reduction techniques.
