{"doi":"10.1111/bju.16685","title":"Statistics in clinical urology research: fundamental concepts in comparative research","abstract":"In this edition of the BJUI Trainees' Corner, we provide an essential summary of key statistical concepts in clinical urology research, offering valuable guidance for residents and fellows when writing scientific manuscripts. Drawing from the comprehensive ‘Guidelines for reporting of statistics for clinical research in urology’ [1], which were adopted by all four major urology journals, this commentary focuses on three fundamental principles from these guidelines when applied to comparative research, for example, when comparing the effects of two treatments on a clinically relevant endpoint in a randomised trial or an observational dataset. These principles are: (1) hypothesis testing, P values, and statistical significance; (2) interpretable estimates and clinical vs statistical significance; and (3) confidence intervals (CIs) and conclusions. These selected principles highlight common statistical misconceptions that can lead to inappropriate conclusions if not properly understood, ensuring that early-career researchers are well equipped to conduct and report their research with rigour and accuracy. For further reading, we recommend studying 3.1, 4.4, 4.5 and 6.3 in the Guidelines. Statistical analyses include two aspects: inference and estimation. Inference is the process of drawing conclusions from data. This is the foundation of hypothesis testing, where researchers analyse data to calculate P values. Inference can be thought of in terms of the question ‘Is something there?”, so for comparative research, inference addresses whether there is indeed a difference between groups. Estimation asks ‘How big is it?’ and so with respect to comparative research, involves calculating effect sizes, such as odds ratios, risk differences, or model coefficients, along with their CIs to quantify the magnitude of differences between groups. Just as a medical researcher would carefully consider biological mechanisms or relevant findings in existing studies, both facets of statistical inference must be thoughtfully addressed to draw appropriate conclusions from research data. Consider that we have performed a hypothesis test to detect a difference in UTI events after giving patients one antibiotic vs another and obtained our P value. Statistical significance is determined based on whether our P value is less than our alpha (0.05 is the value most widely used in medical research). If we find that the P value is < 0.05, our interpretation is straightforward: we can say, for example, ‘we found a statistically significant difference’ or that ‘we found sufficient statistical evidence of a difference’. Interpretation becomes tricky when our P value is not statistically significant (P ≥ 0.05). Often, investigators state that they found ‘no difference’. This is a serious misinterpretation of hypothesis testing: P values are the probability of observing a sample as extreme, or more than is currently observed, under the assumption that the null hypothesis is true for the population being studied. For example, assuming there is no difference in infection rates between two groups of patients who received different antibiotics, the P value is the chance we see a difference as extreme as observed or greater (Table 3). In other words, we consider the null hypothesis as true until we have sufficient evidence to reject it (P < 0.05). When the P value was ≥ 0.05, we did not have sufficient evidence to reject the null hypothesis or, in other words, we failed to reject the null hypothesis. The typical frequentist hypothesis testing framework does not set out to prove the null hypothesis but only to assess the level of evidence we have against it. Consequently, P values cannot be interpreted as levels of evidence for the truth of a null hypothesis. It is impossible to prove a negative, and the absence of evidence is not evidence of absence. The analogy in the Statistical Guidelines, rule 3.1 ‘Do not accept the null hypothesis’, is helpful here to conceptualise ","journal":"British Journal of Urology","year":2025,"id":562362,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9506,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":253868,"name":"Sigrid Carlsson","orcid":"0000-0003-3553-5710","position":1,"is_corresponding":false},{"id":485721,"name":"Melissa Assel","orcid":null,"position":0,"is_corresponding":true}],"reference_count":1,"raw_metadata":null,"created_at":"2026-07-19T02:56:05.550545Z","pmid":"39972944","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":[]}