{"doi":"10.1177/0193841x20977619","title":"Reporting Bayesian Results","abstract":"<jats:p>Because of the different philosophy of Bayesian statistics, where parameters are random variables and data are considered fixed, the analysis and presentation of results will differ from that of frequentist statistics. Most importantly, the probabilities that a parameter is in certain regions of the parameter space are crucial quantities in Bayesian statistics that are not calculable (or considered important) in the frequentist approach that is the basis of much of traditional statistics. In this article, I discuss the implications of these differences for presentation of the results of Bayesian analyses. In doing so, I present more detailed guidelines than are usually provided and explain the rationale for my suggestions.</jats:p>","journal":"Evaluation Review","year":2020,"id":33799,"datarank":0.49403525297943734,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"self_citation_contribution":0.31191623125197543,"citation_network_contribution":0.18211902172746192,"self_endowment_contribution":0.31191623125197543,"citer_contribution":0.18211902172746192,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":7,"citers_with_citation_signal":6,"citers_with_endowment":6,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":176145,"name":"David Rindskopf","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"33380224","pmcid":null,"openalex_id":"https://openalex.org/W3116106063","authors":[],"funders":[],"total_grants":0,"fwci":1.1077,"citation_percentile":0.80684105,"influential_citations":0,"citation_trend":[{"year":2022,"count":1},{"year":2023,"count":4},{"year":2024,"count":1},{"year":2025,"count":1}],"oa_status":"closed","license":"https://journals.sagepub.com/page/policies/text-and-data-mining-license","oa_locations":[{"url":"https://journals.sagepub.com/doi/pdf/10.1177/0193841X20977619","host_type":"publisher"},{"url":"https://journals.sagepub.com/doi/full-xml/10.1177/0193841X20977619","host_type":"publisher"},{"url":"https://doi.org/10.1177/0193841x20977619","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/33380224","host_type":"repository"},{"url":"https://journals.sagepub.com/doi/10.1177/0193841X20977619","host_type":"repository"}],"fields_of_study":["Statistical Methods and Bayesian Inference","Bayesian Methods and Mixture Models","Statistical Methods and Inference","Computer Science","Medicine","Mathematics","Bayes Theorem","Guidelines as Topic","Research Design"],"mesh_terms":["Bayes Theorem","Research Design","Guidelines as Topic"],"keywords":["Frequentist inference","Bayesian statistics","Bayesian probability","Statistics","Frequentist probability","Econometrics","Presentation (obstetrics)","Computer science","Mathematics","Bayesian inference","Medicine","Reporting Guidelines","Reporting Standards"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-09T16:56:41.297196Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}