BEGIN:VCALENDAR PRODID:-//github.com/rianjs/ical.net//NONSGML ical.net 4.0//EN VERSION:2.0 BEGIN:VEVENT DESCRIPTION:Use coupon COMPLIANCE15 to get 15% discount on this virtual se minar\n\nThis seminar bridges that gap. Designed specifically for non-sta tisticians\, it provides a clear\, practical introduction to biostatistic s without overwhelming you with mathematics. You’ll learn the “why” behin d the numbers\, gain tools to spot meaningful findings\, and avoid being misled by misused statistics. By the end of the program\, you’ll be able to read statistical reports with confidence\, communicate findings to col leagues and stakeholders\, and make better data-driven decisions that can shape the future of clinical and biotech innovation.\n\nWhy Should You A ttend:\nIn the world of clinical research and biotechnology\, numbers car ry weight. A single statistical finding can influence whether a project m oves forward\, a regulatory submission is approved\, or a study is deemed credible. Yet for many professionals\, statistics can feel like a barrie r rather than a tool. Questions often linger: Was the right test used? Is this “significant” result actually meaningful? Am I interpreting this re port correctly?\n\nThese uncertainties can create hesitation and reduce c onfidence in decision-making. While statisticians may handle the technica l details\, non-statisticians are frequently expected to review reports\, contribute to study design discussions\, and communicate findings to col leagues\, regulators\, or clients. Without a clear understanding of stati stical concepts\, it can be easy to misinterpret results or miss importan t nuances.\n\nThis seminar is designed to help bridge that gap. In two fo cused days\, you’ll be introduced to the key ideas and methods that appea r most often in clinical and biotech research. The emphasis is on interpr etation and application\, not heavy mathematics. You’ll see how statistic al results fit into the bigger picture of study design\, regulatory requi rements\, and scientific communication.\n\nBy attending\, you will:\n\n\n Develop a clearer understanding of common statistical terms and concepts. \nGain practical insight into how and why certain tests are applied.\nLea rn approaches for distinguishing between statistically significant and cl inically meaningful results.\nImprove your ability to follow discussions with statisticians and regulatory professionals.\nIncrease your confidenc e when reviewing\, reporting\, or presenting statistical information.\n\n This seminar is not about turning you into a statistician. Rather\, it is about equipping you with enough knowledge to understand the essentials\, ask informed questions\, and engage more effectively in the decision-mak ing process. For professionals who work with clinical data but don’t have formal statistical training\, this program provides a practical and supp ortive way to reduce uncertainty and build confidence.\n\nWho will Benefi t:\n\nPhysicians\nClinical Research Associates\nClinical Project Managers /Leaders\nSponsors\nRegulatory Professionals who use statistical concepts /terminology in reporting\nMedical Writers who need to interpret statisti cal reports\nClinical Investigators / Principal Investigators (PIs)\nMedi cal Affairs Professionals\nSafety and Pharmacovigilance Staff\nData Manag ers and Data Analysts in clinical trials\nHealthcare Policy Analysts\nPha rmaceutical &\; Biotechnology Executives and Managers\nGraduate Studen ts in the biological and medical sciences\nAnyone interested in learning core concepts and application of statistics\n\nDay 1 introduces participa nts to the fundamental building blocks of statistical reasoning. The day begins with an exploration of why statistics matter in research and healt hcare decision-making. Participants will examine the essential distinctio n between samples and populations\, learn why variability and uncertainty must be accounted for\, and gain insight into what statistics can — and cannot — accomplish. By dispelling the myth of the statistician as a “mag ician\,” this session underscores the value of clear\, transparent method s over mysterious or opaque analyses.\n\nThe day continues with an in-dep th look at the many ways data can be interpreted. Key concepts such as p- values\, effect sizes\, and confidence intervals are explained in plain l anguage\, along with the differences between statistical and clinical sig nificance. By demystifying these measures\, attendees will be better equi pped to recognize whether findings are not only statistically valid but a lso practically meaningful in real-world contexts.\n\nAttention then turn s to the application of common statistical tests. Participants will learn why testing is necessary through the lens of Null Hypothesis Significanc e Testing (NHST)\, and will gain familiarity with comparative tests\, cor relation methods\, multiple regression analysis\, and non-parametric tech niques. Rather than teaching calculations\, this session emphasizes under standing when and why each method is used\, along with the strengths and limitations of each approach. The day concludes with an introduction to B ayesian logic\, which offers a different perspective on statistical think ing. Attendees will see how Bayesian methods can provide richer insights into diagnostics\, genetics\, and other areas where uncertainty plays a c entral role.\n\nDay 2 builds on this foundation of Day 1 with applied and forward-looking topics. The day begins with a guided exercise in interpr eting systematic reviews\, a cornerstone of evidence-based medicine. Part icipants will learn how to evaluate statistical language across multiple studies\, identify sources of bias\, and judge the transparency and repro ducibility of reported findings. Emphasis is placed on developing the abi lity to communicate insights clearly to both technical and non-technical audiences.\n\nThe next session explores study power and sample size — cri tical considerations in trial design and regulatory submissions. Attendee s will review how concepts such as p-values\, effect size\, and significa nce levels come together in sample size calculations\, and will be introd uced to available formulas\, software tools\, and practical resources for applying these concepts in real projects.\n\nThen\, participants will le arn the essential steps in developing a Statistical Analysis Plan (SAP). Using FDA guidance as a foundation\, the session walks through how to des ign a plan that ensures clarity\, compliance\, and rigor. A template SAP is provided to give attendees a concrete starting point for their own wor k.\n\nThe seminar closes with a discussion of specialized topics and an o pen Q&\;A. Here\, participants will explore survival analysis methods\ , applications in pharmacokinetics and pharmacodynamics\, and strategies for taking a holistic view of study design and interpretation. This final session ties together the threads of the seminar\, encouraging participa nts to approach data with both confidence and critical thinking.\n\nBy th e end of the two days\, attendees will not only understand the language a nd logic of biostatistics but will also be able to apply these concepts t o their daily work. They will leave better prepared to evaluate published research\, collaborate with statisticians\, develop study plans\, and co mmunicate statistical findings clearly to colleagues\, regulators\, and o ther stakeholders. Most importantly\, they will gain the assurance that s tatistics are not a barrier\, but rather a powerful tool to improve decis ion-making and advance clinical and scientific discovery.\n DTEND:20260220T140000 DTSTAMP:20260512T232941Z DTSTART:20260219T090000 LOCATION:\, SEQUENCE:0 SUMMARY:Use coupon COMPLIANCE15 to get 15% discount on this virtual semina r\nThis seminar bridges that gap. Designed specifically for non-statistic ians\, it... 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