Assumed sensor independence, adjust joint likelihoods to account for dependence

Overestimated lab likelihoods, replace with real-field conditional probabilities

Misinterpreted correlated sensor readings, incorporate environmental covariates into inference model

Underestimated minor leak prior, increase prior probability of H2

It depends heavily on subjective probability assignments.

It assumes hypotheses are exhaustive and mutually exclusive.

It struggles to incorporate prior knowledge or context.

It can generally assess only two hypotheses at a time.

It typically involves simpler computations and fewer assumptions.

It ensures error-free decision making based on observed evidence.

It eliminates the need to model uncertainty with probability distributions.

It updates prior beliefs using observed data to revise the probability of a hypothesis.

It is equal to the sum of the likelihood function and the prior probability.

It is proportional to the product of the likelihood function and the prior probability.

It is equal to the likelihood function divided by the prior probability.

It is equal to the prior probability divided by the likelihood function.

The model used to estimate the test statistic from the sample data.

The specific claim about the population that is being tested.

The assumption that there is no effect or no difference.

The alternative explanation that challenges the initial assumption.

The likelihood of rejecting a true hypothesis due to statistical error.

The initial probability assigned to a hypothesis based on prior knowledge or assumptions.

The updated probability of a hypothesis after incorporating new data.

The probability of the evidence occurring if the hypothesis is true.

The likelihood that a transmitted symbol is incorrectly decoded.

The maximum number of bits required to encode any outcome of the variable.

The capacity of a noiseless communication channel.

The minimum average number of bits needed to describe the variable's outcomes.