A statistical method that applies Bayes' theorem, allowing for the incorporation of prior knowledge into data analysis.

A subset of artificial intelligence focused on algorithms that enable computers to learn from and make predictions based on data.

The systematic plan for conducting research, encompassing design, data collection, and analysis techniques.

The principles governing the ethical use of data, ensuring integrity, transparency, and respect for privacy in research.

A mathematical representation of observed data, used to infer relationships and make predictions.

In Bayesian analysis, the distribution representing initial beliefs about a parameter before observing data.

The updated distribution of a parameter after observing data, combining prior distribution and likelihood.

A function that measures the plausibility of a parameter given the observed data, crucial in Bayesian analysis.

A technique used to determine how different values of an input affect a given output in a model.

A statistical method for testing a hypothesis about a parameter by comparing data against a null hypothesis.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

The process of choosing the most appropriate machine learning algorithm based on the specific research problem.

The graphical representation of information and data to facilitate understanding and insights.

The ability to achieve consistent results using the same methods and data, crucial for research integrity.

A set of principles designed to guide researchers in conducting their work responsibly and ethically.

A process where research is evaluated by experts in the field before publication, ensuring quality and credibility.

A concise summary of a larger report, highlighting key findings and recommendations for a non-technical audience.

The process of planning and organizing tasks to ensure timely completion of research projects.

The systematic gathering of information for analysis, crucial for the validity of research findings.

The process of confirming that a statistical model accurately represents the data it is intended to describe.

Strategies for simplifying and effectively presenting intricate statistical concepts to diverse audiences.

Research projects that push boundaries, integrating new methods or technologies to solve complex problems.

The process of comparing two or more models or datasets to determine their relative performance.

The ability to effectively convey information and engage an audience during a presentation.

A comprehensive document summarizing the research project, findings, and implications for stakeholders.