👉 Hypothesis computing is a theoretical framework that combines elements of hypothesis testing, Bayesian inference, and machine learning to provide a more flexible and efficient approach to statistical inference and decision-making. It posits that instead of relying solely on frequentist or Bayesian methods, hypothesis testing can be viewed as a sequential process where hypotheses are continually updated based on incoming data. This approach allows for the incorporation of prior knowledge and the ability to make decisions in real-time, making it particularly useful in dynamic environments such as online learning, adaptive clinical trials, and real-time data analysis. By treating hypotheses as hypotheses to be tested and refined, hypothesis computing aims to improve the accuracy and efficiency of statistical inference while reducing the need for large sample sizes or extensive computational resources.
