{"doi":"10.1111/j.1467-842x.1979.tb01147.x","title":"OPTIMAL TESTS OF SIGNIFICANCE","abstract":"<jats:title>Summary</jats:title><jats:p>To perform a test of significance of a null hypothesis, a test statistic is chosen which is expected to be small if the hypothesis is false. Then the significance level of the test for an observed sample is the probability that the test statistic, under the assumptions of the hypothesis, is as small, or smaller than, its observed value. A “good” test statistic is taken to be one which is stochastically small when the null hypothesis is false. Optimal test statistics are defined using this criterion and the relationship of these methods to the Neyman‐Pearson theory of hypothesis testing is considered.</jats:p>","journal":"Australian Journal of Statistics","year":1979,"id":21644,"datarank":0.4524917815647024,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.21107609469958727,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.21107609469958727,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":4,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":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":138588,"name":"J. Robinson","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"24259432","pmcid":null,"openalex_id":"https://openalex.org/W1970221037","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.07211698,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":"http://onlinelibrary.wiley.com/termsAndConditions#vor","oa_locations":[{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1467-842X.1979.tb01147.x","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-842X.1979.tb01147.x","host_type":"publisher"},{"url":"https://doi.org/10.1111/j.1467-842x.1979.tb01147.x","host_type":"journal"}],"fields_of_study":["Advanced Statistical Methods and Models","Fault Detection and Control Systems","Advanced Statistical Process Monitoring","Mathematics"],"mesh_terms":[],"keywords":["Test statistic","Null hypothesis","One- and two-tailed tests","p-value","Statistics","Statistical hypothesis testing","Pearson's chi-squared test","Alternative hypothesis","Mathematics","Statistic","Statistical significance","Null (SQL)","Z-test","F-test","Test (biology)","Null distribution","Computer science","Data mining"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-06T16:26:59.147666Z","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":[]}