Statistical probability

Null Hypothesis ($H_0$)

A statistical statement proposing that there is no significant difference or association between observed data and expected theoretical outcomes (e.g., following a specific Mendelian ratio).

Chi-Square ($\chi^2$) Test / Goodness of Fit

A statistical test used to determine whether observed categorical frequencies differ significantly from expected frequencies.

Degrees of Freedom ($df$)

The number of independent values or classes that can vary in an analysis, calculated as $n - 1$ (where $n$ is the number of phenotypic classes).

Significance Level ($\alpha$) / Threshold of Significance

The predetermined threshold (commonly $\alpha = 0.05$) used to decide whether to reject the null hypothesis.

Critical Value

The threshold value from a statistical distribution table; if the calculated test statistic exceeds this value, the null hypothesis is rejected.

Prevalence

The proportion of a population found to have a specific condition or disease at a given time.

False Positive Rate

The probability that a test incorrectly indicates the presence of a condition when it is absent.

False Negative Rate

The probability that a test fails to indicate a condition when it is actually present.

Bayes' Theorem / Conditional Probability ($P(A\vert{}B)$)

A theorem describing the probability of an event, based on prior knowledge of conditions related to the event.

Product Rule (And Rule)

The principle that the probability of two independent events occurring together is the product of their individual probabilities.

Sum Rule (Or Rule)

The principle that the probability of either of two mutually exclusive events occurring is the sum of their individual probabilities.

Multinomial / Binomial Probability

Mathematical formulas used to calculate the probability of specific combinations of multiple independent categorical outcomes across a series of trials.