The phrase "a distribution can be" is correct and usable in written English.
It can be used when discussing the characteristics or properties of a distribution in statistics or probability.
Example: "In statistics, a distribution can be normal, skewed, or uniform, depending on the data set."
Alternatives: "a distribution may be" or "a distribution could be".
This parsimonious principle (the "Pareto principle") tells us that a distribution can be said to be beneficial only if some people are made better off while no one is made worse off.
The standard deviation of a distribution can be specified as an expected effect size, which means, in the case of a half-normal distribution, smaller values are more likely and plausible values have been effectively represented between zero and twice the effect size.
Some of the most important features and characteristics of a distribution can be studied through moments.
Skewness and kurtosis of a distribution can be measured by β 1 = μ 3/σ 3 and β 2 = μ 4/σ 4, respectively.
To address this issue, a distribution can be assigned to the probability of detection for a given measurement (Cohen and Ryan 1989).
It should be remembered in interpreting such effects that Cohen's d describes differences in terms of the means, whereas proportions in the tails of a distribution can be much larger.
As a consequence, a probability distribution can be obtained that leads directly to the definition of an entropy of the system under consideration (Equation (3) and Equation (4)).
Furthermore, an asymptotic a posteriori distribution can be determined.
We mention how probabilities can be estimated from a given data set and how a probability distribution can be approximated using a fuzzy histogram technique.
Instead, when some knowledge suggesting the expected value of a parameter is available, a normal distribution can be used.
The pixel intensities appear to be roughly normally distributed, but an exponential distribution can be fitted to those above a chosen threshold intensity.
