👉 Likelihood math is a branch of probability theory that deals with quantifying the probability of different outcomes in a random experiment, particularly focusing on how well observed data fits a model. It uses the concept of likelihood functions, which are mathematical expressions that measure how likely it is to observe a given set of data under a specific model. The likelihood ratio, a key tool in this field, compares the likelihood of the data under two competing hypotheses (e.g., a simpler model versus a more complex one). A higher likelihood ratio suggests stronger evidence for the simpler model, indicating that the observed data is more likely under the simpler model. This method is crucial in statistical inference, aiding in hypothesis testing and model selection by providing a quantitative measure of how well different models explain the data.
