Podcasts about Statistical significance

Is your message testing just throwing random ingredients into a pot and hoping it tastes good? In this no-holds-barred episode, the boys tackle the chaos of message testing and why most PMMs are doing it wrong. From useless A/B tests with tiny sample sizes to executives demanding "just one more tweak," we dive into what actually works. Tune in for laughs, practical frameworks, and why your mom's cooking method isn't a valid messaging strategy. We're covering:Only 30% of product marketers have ever actually tested a message (and most of them did it wrong)Why A/B testing is the "let's not commit" cop-out of marketing teamsThe one-variable rule: why testing multiple things at once is destroying your resultsThe critical difference between message testing and A/B testing that most marketers missHow to build a message testing framework that gets leadership buy-in immediatelyIf you've ever sat through a meeting where someone suggested "Let's A/B test it" for a campaign going to 150 people, this episode will either validate your frustration or completely change how you work. Either way, your next messaging project just got a whole lot more effective.Timestamps01:39 Question: How Many PMMs Actually Test Messages?02:31 Why A/B Testing Is Often the Wrong Approach04:00 Statistical Significance and Sample Size Problems05:58 Testing Text vs. Testing Visuals06:05 Messaging as Strategy vs. Tactical Implementation  09:01 Differentiating Message Testing from A/B Testing12:15 The Commitment Problem in Marketing Decision-Making16:16 How Trade Shows Can Validate Positioning and Messaging19:45 Message Testing to Define Product Uniqueness22:00 Eric's Process Misstep: Jumping to Experimentation Too Soon27:45 The Scientific Method in Message Testing29:01 The Gumbo Analogy: Why Most Marketing Isn't Replicable34:15 Isolating Variables in Message Testing35:36 Building a Strong Hypothesis Framework38:25 Gab's Message Testing Resources (Message Market Fit)41:30 The ROI of Message Testing and Business Consequences44:35 Final Thoughts and Key TakeawaysShow notes:Victoria Rudi's Messaging FrameworksMessage Market Fit - Gab's resource for message testing frameworks and templates.Freckle.io - The Clay alternative talked in the example Hosted by Ausha. See ausha.co/privacy-policy for more information.

Today, we're thrilled to have two of Acadia's brightest minds in organic optimization join us. First, we have Brittany Flanagan, Head of CRO at Acadia, with over 15 years of growth marketing experience across brands and agencies. Joining her is Julian Galindo, Senior Account Manager on our Retail Team and a recognized expert in driving organic growth on Amazon. Together, they delve into the intricate world of Conversion Rate Optimization (CRO) and its implications for both Amazon and D2C marketplaces. From tackling data-driven decisions to leveraging AI for personalized experiences, this conversation highlights how CRO techniques are evolving and what the future holds for e-commerce.   KEY TAKEAWAYS In this episode, Julie, Jordan, Brittany, and Julian discuss: The concept of Conversion Rate Optimization (CRO) as the process of using data to understand customer behavior and optimize websites to increase conversions. CRO techniques for product pages on Amazon and the importance of data-driven experimentation. How the availability of data and technology has evolved, impacting CRO strategies for both Amazon and D2C. The role of AI in modern CRO, particularly in terms of personalization and recommendation algorithms. Amazon vs. D2C in CRO: Differences in data accessibility and testing capabilities between Amazon's ecosystem and direct-to-consumer (D2C) platforms. Limitations on Amazon, where multivariate testing isn't feasible compared to the more granular data tracking available in D2C. Addressing the complexity of coordinating across various departments like marketing, dev, and creative teams for effective CRO implementation. The importance of creating data-driven, iterative processes to understand the nuances of consumer behavior and preferences. Insights into the potential future developments in CRO, including better AI-driven personalization and enhanced data reporting on Amazon. Speculations on how CRO tools and practices will become more accessible for medium-sized and smaller businesses.

Null hypothesis significance testing (NHST) is the default approach to statistical analysis and reporting in marketing and, more broadly, in the biomedical and social sciences. A Journal of Marketing study advocates for rethinking this approach entirely. Read an in-depth recap of this research here: https://www.ama.org/2023/12/12/time-to-abandon-null-hypothesis-significance-testing-moving-beyond-the-default-approach-to-statistical-analysis-and-reporting/ Read the full Journal of Marketing article here: https://doi.org/10.1177/00222429231216910 Reference: Blakeley B. McShane, Eric T. Bradlow, John G. Lynch, Jr., and Robert J. Meyer, “‘Statistical Significance' and Statistical Reporting: Moving Beyond Binary,” Journal of Marketing. Narrator: Adalgisa Butkewitsch Acknowledgments: Sushma Kambagowni Topics: p-values, statistics, statistical significance, research The JM Buzz Podcast is a production of the American Marketing Association's Journal of Marketing and is produced by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠University FM

In this ‘Greatest Hits' episode of Getting to Aha!, host Darshan Mehta is joined by Stephen Griffiths, Director of Insights & Strategy at Post Consumer Brands. You'll learn why, when gathering data, you must strike a balance between qualitative and quantitative data, get the lowdown on why insights need to be broader than just market research, and uncover new, valuable insights for your company!

Patrick Jones talks about the transfer portal in this podcast episode. He explains how he aligns topics with the time of year and why he's discussing the transfer portal now. He shares insights based on his experience working with players navigating the transfer process, highlighting common scenarios and misconceptions. He emphasizes the importance of understanding why players want to transfer and the realities they face in finding new opportunities. Patrick delves into the criteria college coaches consider when evaluating transfer candidates, including performance metrics like strikeout-to-walk ratios for pitchers and offensive production for hitters. He also discusses the implications of different types of scholarship offers and the significance of summer collegiate baseball leagues for player exposure and recruitment. Throughout the episode, Patrick offers practical advice and cautions against unrealistic expectations in the transfer process.Timestamps:[01:49] Understanding the Transfer Portal[09:43] NCAA Rules and Regulations for Division I Baseball[12:29] Expectations & Statistical Significance in the Transfer Portal[19:37] Returning to School, Division Choices, and Scholarship Types[25:55] Summer Collegiate BaseballFollow Patrick Jones on Twitter: @pjonesbaseball Hosted on Acast. See acast.com/privacy for more information.

Is it time for marketers and researchers to abandon null hypothesis significance testing? A new Journal of Marketing study says yes. Join host Christopher Bechler (University of Notre Dame) for a fascinating discussion about a new Journal of Marketing study that advocates for a major transition in statistical analysis and reporting. He talks with study authors Blake McShane, Eric Bradlow, John Lynch, and Robert Meyer, as well as Fred Feinberg, about breaking away from the stubborn focus on a p-value's position relative to .05. Reference: Blakeley B. McShane, Eric T. Bradlow, John G. Lynch, Jr., and Robert J. Meyer, “‘Statistical Significance' and Statistical Reporting: Moving Beyond Binary,” Journal of Marketing. Fred Feinberg's commentary is available here. Read a quick recap of this research: https://www.ama.org/2023/12/12/time-to-abandon-null-hypothesis-significance-testing-moving-beyond-the-default-approach-to-statistical-analysis-and-reporting/ Topics: research, statistical significance, p-values, replication, science The JM Buzz Podcast is a production of the American Marketing Association's Journal of Marketing and is produced by ⁠⁠University FM

It's mentioned on the podcast pretty much every week. But what does “statistical significance” actually mean? In this episode of The Studies Show, Tom and Stuart start 2024 off with the most exciting subject possible: p-values. THRILL as they discuss statistical misconceptions! MARVEL as they talk about how “effect size” differs from “statistical significance”! CHUCKLE as they resort to endless coin-flipping analogies! And GASP as they discuss ways to stop scientists from “hacking” their p-values and ending up with misleading research!The Studies Show is brought to you by Works in Progress magazine - an online magazine full of essays about science, technology, and human progress. Works in Progress is the kind of magazine that makes its readers massively better-informed about every subject it covers, with deeply-researched articles by experts in the relevant fields - and it's all free. Check it out at their site right here.Show notes* 89% of psychology textbooks get p-values wrong* Letter on how the research on power-posing went wrong* The classic “false-positive psychology” paper on how p-hacking can get you any result you want* The FiveThirtyEight online p-hacking tool* The “p-curve” method for detecting p-hacking* How p-hacking is just “overfitting” by another name* List of weasel terms like “approaching significance”* Reading on a screen before bed “might be killing you”!* The (much less scary) relevant study* Tom's BuzzFeed News article on the idea to lower the p-value threshold to 0.005* The original paper, plus the response arguing that scientists should “justify their alpha”* Registered Reports, and how they can deter p-hackingCreditsThe Studies Show is produced by Julian Mayers at Yada Yada Productions. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.thestudiesshowpod.com/subscribe

This vintage episode of Vitality Radio originally aired on October 16th, 2021 but the information is still accurate and important. On this episode, Jared focuses on the theory behind antidepressants. Are they effective? Around 13-17% of American adults are currently taking antidepressants. Jared deep dives into the research specifically conducted by Harvard Medical School researcher Irving Kirsch. These studies detail that antidepressants function as a placebo in the vast majority of people. Jared explains why these important studies show that the problematic diagnosis framework, unethical pharma companies + broken FDA approval process, are not actually helping people with their mental and emotional health.Additional Information:#164 Psychobiotics - Unique Probiotics for Depression, Anxiety and More Part 1#166 Psychobiotics - Unique Probiotics for Depression Anxiety and More Part 2#327: The Natural Approach to Mental Health: How To Optimize Mood and Reduce Anxiety With Lifestyle and SupplementsVisit the podcast website here: VitalityRadio.comYou can follow @vitalityradio and @vitalitynutritionbountiful on Instagram, or Vitality Radio and Vitality Nutrition on Facebook. Join us also in the Vitality Radio Podcast Listener Community on Facebook. Shop the products that Jared mentions at vitalitynutrition.com. Let us know your thoughts about this episode using the hashtag #vitalityradio and please rate and review us on Apple Podcasts. Thank you!Please also join us on the Dearly Discarded Podcast with Jared St. Clair.Just a reminder that this podcast is for educational purposes only. The FDA has not evaluated the podcast. The information is not intended to diagnose, treat, cure, or prevent any disease. The advice given is not intended to replace the advice of your medical professional.

In this episode of How I Grew This, Anthony Scarpaci joins Mada Seghete to discuss the differences between attribution and incrementality when measuring marketing performance, "business significance" versus statistical significance, and the importance of testing in the growth context. Anthony is the VP of Growth at Acorns. He has been in growth and performance marketing for over ten years, driving efficient growth for businesses across telecom, financial services, food, and travel industries. Before joining Acorns, Anthony held various marketing roles at companies like NerdWallet, Rustic Pathways, PURE Group of Insurance Companies, Blue Apron, Betterment, and Dish Network.

Today, we welcome back one of our special guests, Dr. Elizabeth Sweeney, who is a biostatistician and Assistant Professor of Biostatistics at Penn Medicine. In today's episode, she will discuss the topic of Statistical Significance alongside one of our hosts, Dr. Alonso Carrasco-Labra.Elizabeth and Alonso delve into the topic and discuss how Elizabeth tests for statistical significance in her daily practice, common misconceptions with statistical significance, and possible problems with multiple testing.To view this episode's corresponding video of "Statistics with Hans and Hera," please visit the following link: https://youtu.be/fM_C_kQtdtU

Shownotes Wilson, E. B. (1923). The Statistical Significance of Experimental Data. Science, 58(1493), 93–100. https://doi.org/10.1126/science.58.1493.93 van Dongen, N. N. N., & van Grootel, L. (2021). Overview on the Null Hypothesis Significance Test. https://doi.org/10.31234/osf.io/hwk4n Stark, P. B., & Saltelli, A. (2018). Cargo‐cult statistics and scientific crisis. Significance, 15(4), 40-43. Uygun Tunç, D., Tunç, M. N., & Lakens, D. (2023). The epistemic and pragmatic function of dichotomous claims based on statistical hypothesis tests. Theory & Psychology, 09593543231160112. https://doi.org/10.1177/09593543231160112 Bakan, D. (1966). The test of significance in psychological research. Psychological Bulletin, 66(6), 423–437. https://doi.org/10.1037/h0020412 Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45(12), 1304–1312. https://doi.org/10.1037/0003-066X.45.12.1304 Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49(12), 997–1003. https://doi.org/10.1037/0003-066X.49.12.997 Cohen, J. (1995). The earth is round ( p 

Uncovering the Truth: The Statistical Significance of the Resurrection. Explore the Genesis of God, Mantle of Christ, and Purpose of Resurrection. Discover the Power of Being In Christ, Sin's Impact on Your Life, and the Hope the Resurrection Brings. Is the Resurrection statistically probable? Join Us for a Mind-Blowing Journey.    https://youtube.com/live/atiK85G1SRk

The so-called “Santa Claus Rally” is an old adage that referred to the above-average market performance that occurred in the last week of the year (between Christmas and New Years). What is the statistical significance of this phenomenon, if any?

The so-called “Santa Claus Rally” is an old adage that referred to the above-average market performance that occurred in the last week of the year (between Christmas and New Years). What is the statistical significance of this phenomenon, if any?

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In this episode of How I Grew This, Anthony Scarpaci joins Mada Seghete to discuss attribution and incremental measurement, business significance versus statistical significance when it comes to measurement, and the importance of testing in the growth context. Anthony is the VP of Growth at Acorns. He has been in growth and performance marketing for over ten years, driving efficient growth for businesses across telecom, financial services, food & beverage, and travel education industries. Before joining Acorns, Anthony held various marketing roles in companies like NerdWallet, Rustic Pathways, PURE Group of Insurance Companies, Blue Apron, Betterment, and Dish Network.

全新系列啟動（雖然半年前就錄好了） 這系列我們將透過JOSPT的Evidence in Practice系列文章 加上Dr. Frank的獨到見解來幫助大家連結學術與臨床 這集我們會告訴大家difference、change、和p value解讀上常見的謬誤 喜歡的話記得留言告訴我們，並按讚、分享、訂閱我們的podcast喔！ 下週過年期間休更一週，預祝大家新年快樂！ Timecode: 00:27 想做這個系列的原因是Frank的執著 08:56 原來被2PRO PT訪問是讓你成為教授的跳板？ 12:58 第一篇：如何解讀outcome? Difference和change的差別是？ 16:01 Frank佩服這系列作者戳破你被學術文章誘導的套路，以及更深入的對difference和change的解讀 24:06 第二篇：統計上的差異p value與臨床的應用 28:08 作者建議臨床治療師應該看什麼樣的數據呢？MIC, MCID是你的好幫手 31:53 Frank的補充時間（內含很多的統計概念，重要！） 39:36 Roger的貼心小統整 40:20 樣本數越多就越容易顯著？真假?! 42:15 Frank補充大家常常對p value的誤解 44:43 本集總結 歡迎到各平台追蹤或來信來訊跟我們提出疑問～ Facebook: https://www.facebook.com/2PROPT/ Instagram: https://www.instagram.com/2pro_pt/ Email: 2propt@gmail.com 也可以在此收聽： Apple podcast: https://tinyurl.com/y97q7tms Spotify: https://tinyurl.com/ydavzqxu Google podcast: https://tinyurl.com/yd86pbcl YouTube channel: https://tinyurl.com/y82ewo5b Music by Elizabeth's Groove by Amarià @amariamusique Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: bit.ly/elizabeths-groove Music promoted by Audio Library youtu.be/-MO-mrBlo5s Reference: Kamper SJ. Interpreting Outcomes 1—Change and Difference: Linking Evidence to Practice. J Orthop Sports Phys Ther 2019;49(5):357–358 Kamper SJ. Interpreting Outcomes 2—Statistical Significance and Clinical Meaningfulness: Linking Evidence to Practice. J Orthop Sports Phys Ther 2019;49(7):559–560.

Episode 202- Dr. Dean Radin! Dr Radin is a parapsychologist and author of several books on psi phenomena. His research has also been published in over 100 scientific journals.  In this episode he talks about the experiments he has done as well as being a part of “Project Stargate.”  I also get his thoughts on synchronicity, mediums, near death experiences and much more! 0:00:00 - Intro0:01:41 - Dean's Background & Credibility0:04:16 - Topic of Psychic Phenomena 0:06:36 - Statistical Significance of Experiments 0:10:11 - Demonic Possession & Hauntings 0:13:11 - Witchhunts, Taboo & Uncertainty 0:18:07 - Affirmations & Positive Thinking 0:21:05 - Meditation & Neuroscience 0:21:50 - Synchronicity 0:25:45 - Remote Viewing & Espionage 0:30:05 - Government Programs & MK Ultra 0:32:40 - Evidence and Usefulness of Remote Viewing 0:40:35 - Experiments with Intention & Buddhist Monks 0:43:52 - Experiment with Voodoo Dolls 0:47:12 - Near Death Experiences 0:54:40 - Mediums Talking to the Dead 1:00:10 - Institute of Noetic Sciences 1:00:50- Wrap Up Dean Radin website:https://www.deanradin.comInstitute of Noetic Sciences website:https://noetic.orgChuck Shute website:http://chuckshute.comSupport the show (https://venmo.com/Chuck-Shute)

The Oncology Journal Club - Delivering Oncology News DifferentlyThe Oncology Podcast, brought to you by Oncology News Australia, is proud to present Episode 48 in our series The Oncology Journal Club.Welcome to another entertaining and informative episode. We're all about the P's this week! Procrastination, P Values and yes, peeing.Eva Segelov talks us through avoiding common P Value pitfalls and understanding statistical significance. Craig Underhill looks at Bone Resorption Inhibitors in Metastatic Castration-Resistant Prostate Cancer. And Hans Prenen talks us through a fascinating study exploring how EGFR activation limits the response of liver cancer to Lenvatinib.As ever, you'll find links to all the papers, bios and twitter handles in the notes on our website.With the usual top quality banter, papers you won't hear of anywhere else and expert analysis from our Hosts, you are in for another great episode of The Oncology Journal Club!Full bios and the list of all papers discussed are available on our website.For the latest oncology news visit www.oncologynews.com.au and for regular oncology updates for healthcare professionals, subscribe for free to get the weekly The Oncology Newsletter.The Oncology Podcast - An Australian Oncology Perspective

If you’ve ever read a medical research study, even just the headline, this podcast is for you. If you’re an EMS educator leading the next generation of EMS providers to our field, this podcast is for you. If you’re a training officer or other EMS professional who presents to colleagues or medical directors, this podcast is for you. You don’t have to be a statistician to interpret all the studies. You don’t even have to be a statistician. Tony Fernandez has been a nationally certified paramedic since 2005. Tony was the second individual to successfully complete the Emergency Medical Services Research fellowship offered by the National Registry of Emergency Medical Technicians. Upon completion of this program,  Tony was a research assistant professor within the Department of Emergency Medicine at the University of North Carolina – Chapel Hill.  Within the department, Tony was the director of Emergency Medical Services (EMS) Research for the EMS Performance Improvement Center, which develops and maintains a comprehensive statewide EMS Data System for North Carolina, South Carolina, and West Virginia. In addition to serving as research director, his own research focuses on patient care in the prehospital environment, EMS occupational health and safety, disaster preparedness, and understanding the EMS workforce. In 2012, Tony was elected as a Fellow for the American Heart Association (AHA).  Tony currently works as a research scientist at ESO.

Just because something is mathematically significant does not mean it is clinically significant. Ashlea Broomfield chats with GP Oliver Frank about ‘statistical significance’ and whether it is time to scrap the term all together.

This episode is also available as a blog post: https://rating.repair/statistical-significance-of-the-a-issuer-rating-for-greensill-bank/

In this episode, Justin interviews the newest coach at Consistency Breeds Growth,  Justyna Dapuzzo, MS., about her epigenetics research and how she applies it to her individualized coaching style for her athletes. Justyna can be contacted at:jmdapuzzo@gmail.com or at Consistency Breeds GrowthFollow Justyna on Instagram: @justyna.dapuzzo@justinamarie.nutritionEpisode Summary0-2:30 Info & Promos05:55 DNA Info Readily Available 07:25 Justyna Explains her research around BPA10:05 BPA Fat Solubility & Processed Food11:30 Changing Paradigm with Saturated Fats15:00 Confounding Factors in Epigenetic Research16:00 Statistical Significance & Study Considerations17:05 What is DNA & What Environmental Factors Impact It?19:00 Protein For Muscle AND DNA!!20:25 Specific Genes & Food Associations24:00 Non-Specific Coaching & Obsolescence 25:00 Keto, As An Example28:00 Study & Sample Size, Funding & Long-Term Considerations30:00 Epigenetics 32:00 Gene Profiles, Training and Informed Performance42:00 How To Get In Contact with JustynaSponsorsConsistency Breeds GrowthUse Promo Code: "CBG"Genopalate (United States Only)Use Promo Code: "CBG"Xendurance-Shop Through This Link!!PermaSleep-Shop Through This Link!!Or: Use "CBG75" for 75$ off any mattressDadBod Fitness Online ProgrammingThe Starving PodcastInstagram:  @the_starving_podcastJustin's Instagram:  @jrome_cbgJordan's Instagram:  @sleepinginonschooldaysGmail: thestarvingpodcast@gmail.comMusic: The New Idea StoreFB: The New Idea StoreGmail: thenewideastore@gmail.com

Part 2 of DATA RULES Guest: Dr. Pete Ludovice of the Georgia Institute of Technology. Playlist at https://www.wrek.org/2016/07/playlist-for-lost-in-the-stacks-from-july-15th-2016-statistical-significance-episode-310/ First broadcast July 15 2016. "Sounds important; I don't know why."

This week we're going to publish an episode every day, discussing how and why your SEO efforts are correlated to your company's earnings per share. Joining us is Doug Bell, the CMO of Searchmetrics, which is an SEO and content marketing platform that helps enterprise scale businesses, monitor their online presence and make data driven decisions. In part 5 of our conversation, we discuss the statistical significance of correlations between SEO and EPS. Show NotesConnect With:Doug Bell: Website // LinkedInThe Voices of Search Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // Twitter

In statistical hypothesis testing, a result has statistical significance when it is very unlikely to have occurred given the null hypothesis.

Are decisions made by scientists one century ago still with us and weigh down science? One decision involves the rules scientists play by when it comes to statistical significance, and more specifically, the rule that results fall below a .05 threshold to count as significant. The point of this threshold is to help minimize false positives. But .05 is consistent with at least 33% of results being false...or worse! Links and Resources * Edouard Machery (https://www.edouardmachery.com/) * The paper (https://imai.fas.harvard.edu/research/files/significance.pdf) * What a nerdy debate about p-values shows about science — and how to fix it (https://www.vox.com/science-and-health/2017/7/31/16021654/p-values-statistical-significance-redefine-0005) * The Alpha War by Edouard Machery (https://osf.io/7uh8a/) * Justify your Alpha by Lakens et al. (https://psyarxiv.com/9s3y6/) * Should We Redefine Statistical Significance? A Brains Blog Roundtable (http://philosophyofbrains.com/2017/10/02/should-we-redefine-statistical-significance-a-brains-blog-roundtable.aspx) * Abandon Statistical Signifcance by McShane et al. (http://www.stat.columbia.edu/~gelman/research/published/abandon.pdf) Paper Quotes "Ronald Fisher understood that the choice of 0.05 was arbitrary when he introduced it. Since then, theory and empirical evidence have demonstrated that a lower threshold is needed. A much larger pool of scientists are now asking a much larger number of questions, possibly with much lower prior odds of success. For research communities that continue to rely on null hypothesis significance testing, reducing the P value threshold for claims of new discoveries to 0.005 is an actionable step that will immediately improve reproducibility." Special Guest: Edouard Machery.

This week we discuss the concept of "thought leaders" and we examine the conclusions about volume CT screening for lung cancer from the NELSON trial. We end with an interview with Dr. Sam Rubinstein, a hematology/oncology fellow at Vanderbilt University Medical Center, on his new paper published in JAMA Network Open titled "Indication of Measures of Uncertainty for Statistical Significance in Abstracts of Published Oncology Trials: A Systematic Review and Meta-analysis". NELSON: doi.org/10.1056/NEJMoa1911793 Measures of Uncertainty: doi.org/10.1001/jamanetworkopen.2019.17530 Back us on Patreon! www.patreon.com/plenarysession

Stephen T. Ziliak is Professor of Economics at Roosevelt University and Conjoint Professor of Business and Law at the University of Newcastle-Australia. A major contributor to the American Statistical Association “Statement on Statistical Significance and P-values” (2016) he is probably best known for his book (with Deirdre N. McCloskey) on The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (2008), showing the damage done by a culture of mindless significance testing, the history of wrong turns, and the benefits which could be enjoyed by returning to Bayesian and Guinnessometric roots.

In our latest episode, Brian welcomes Chief Data Science Officer Andrew Coulson back to the show to discuss the topic of Statistical Significance. Andrew provides an overview of the concept, and explains how taking a binary perspective toward statistical significance can lead to misinterpretation and misinformation. They discuss the American Statistical Association's recommendation to get rid of the term entirely, which is gaining support in the scientific community. They also use point to a recent evidence review which provided a textbook example of ignoring promising study results because of a binary perspective. As always, Andrew provides some takeaways for educators about looking beyond one metric or one study when gathering information on a tool or resource for districts, schools and classrooms.Topics Covered in the Podcast:0:45 Intro3:15 - What is Statistical Significance?4:30 - The Problem with a Binary Mindset6:15 - Misinterpretations and Missing Results9:00 - ASA Moves Away from Statistical Significance12:00 - Factors that Affect Statistical Significance13:20 - Don't Rest on One Result17:30 - TakeawaysThanks for listening to the podcast! Please leave us a review on iTunes, Google Podcasts, Spotify, Spreaker or wherever you are listening to the show. Subscribe to get future episodes as soon as they are released!  

In our latest episode, Brian welcomes Chief Data Science Officer Andrew Coulson back to the show to discuss the topic of Statistical Significance. Andrew provides an overview of the concept, and explains how taking a binary perspective toward statistical significance can lead to misinterpretation and misinformation. They discuss the American Statistical Association's recommendation to get rid of the term entirely, which is gaining support in the scientific community. They also use point to a recent evidence review which provided a textbook example of ignoring promising study results because of a binary perspective. As always, Andrew provides some takeaways for educators about looking beyond one metric or one study when gathering information on a tool or resource for districts, schools and classrooms.Topics Covered in the Podcast:0:45 Intro3:15 - What is Statistical Significance?4:30 - The Problem with a Binary Mindset6:15 - Misinterpretations and Missing Results9:00 - ASA Moves Away from Statistical Significance12:00 - Factors that Affect Statistical Significance13:20 - Don't Rest on One Result17:30 - TakeawaysThanks for listening to the podcast! Please leave us a review on iTunes, Google Podcasts, Spotify, Spreaker or wherever you are listening to the show. Subscribe to get future episodes as soon as they are released!  

Ken Milne is back to discuss statistical significance to celebrate our 150th podcast.  With the different pearls and pitfalls regarding statistical significance, Ken came back on to try and help us navigate through the process and allow us to better understand what this term actually means.

Ron Wasserstein has been the executive director of the American Statistical Association (ASA) since 2007, promoting the practice and profession of statistics. Previously, he was a faculty member of the department of mathematics and statistics at Washburn University in Kansas.The podcast episode focuses on Ron's research article: 'The ASA's Statement on p-Values: Context, Process, and Purpose'. Ron was tasked with leading the creation of a framework outlining how p-values should be used in research an this article was the result. It's had over 300,000 views and over 1,000 citations, and is changing the way researchers approach understanding their results.Find out more about this episode, and our 12-week learning program for researchers at: www.howresearchers.comShare your thoughts on the episode on Facebook, Twitter, and LinkedIn @howresearchers or use #howresearchers

We discuss a survey designed to analyze the extent and root cause of statistical anxiety in the classroom, discussing the methods/limitations of the study. We then talk about yet another crusade against hypothesis testing, this time around the concept of "statistical significance". --- Send in a voice message: https://anchor.fm/databytes/message Support this podcast: https://anchor.fm/databytes/support

When you are running an AB test, one of the most important questions is how much data to collect. Collect too little, and you can end up drawing the wrong conclusion from your experiment. But in a world where experimenting is generally not free, and you want to move quickly once you know the answer, there is such a thing as collecting too much data. Statisticians have been solving this problem for decades, and their best practices are encompassed in the ideas of power, statistical significance, and especially how to generally think about hypothesis testing. This week, we’re going over these important concepts, so your next AB test is just as data-intensive as it needs to be.

Guest Rogue - Tyler Black; News Items: Back to the Moon, Get Rid of Statistical Significance, Alcosynth, Predicting Suicide, Coal vs Renewables; Who's That Noisy; Science or Fiction

Guest Rogue - Tyler Black; News Items: Back to the Moon, Get Rid of Statistical Significance, Alcosynth, Predicting Suicide, Coal vs Renewables; Who's That Noisy; Science or Fiction

This week, a plan to spray antibiotics onto orange trees, and is it time to retire statistical significance? See acast.com/privacy for privacy and opt-out information.

https://accadandkoka.com/wp-content/uploads/2019/01/Mike-Acree-e1547338841282.jpg ()Michael Acree, PhD How do we know that a treatment works or not?  Billions of healthcare dollars are at stake in the answer to that question.  For decades, that answer has largely hinged on theories from a field of human inquiry that combines the precision of mathematics with the accuracy of astrology.  We are talking of course, about statistics and statistical inference. To help us understand better this mystical science, we have as our guest Dr. Michael Acree who has spent his entire career working for the University of California San Francisco as a data scientist and a teacher of statistical science, helping countless researchers make sense of the data they had obtained.  Michael is now retired and is completing a book on the history and philosophy of statistical inference.  He joins us to tell us the whole truth about what is sometimes referred to as the science of mendacity! GUEST: Michael Acree, PhD. LINKS: Michel Accad. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2801944/ (Statistics and the Rise of Medical Fortunetellers) (open access editorial, Texas Heart Institute Journal, 2009) Anish Koka. https://accadandkoka.com/blog/statistical-certainty-less/ (Statistical certainty: Less is more) Stephen Ziliak and Deirdre McCloseky. “https://www.deirdremccloskey.com/docs/jsm.pdf (The Cult of Statistical Significance.)” (2009 , JSM) RELATED EPISODES: https://accadandkoka.com/episode48/ (Ep. 48 Many Statisticians, Many Answers: The Methodological Factor in the Replication Crisis) WATCH ON YOUTUBE: https://youtu.be/FFptM_NTUTk (Watch the episode) on our YouTube channel Support this podcast

Stephen T. Ziliak is Professor of Economics at Roosevelt University and Conjoint Professor of Business and Law at the University of Newcastle-Australia. A major contributor to the American Statistical Association “Statement on Statistical Significance and P-values” (2016) he is probably best known for his book (with Deirdre N. McCloskey) on The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (2008), showing the damage done by a culture of mindless significance testing, the history of wrong turns, and the benefits which could be enjoyed by returning to Bayesian and Guinnessometric roots.

What are Type 1 and Type 2 errors? What is statistical significance, and does it even matter for practical results? How do we judge the quality of a study? How come researchers don't often test more than 2 or 3 groups at once? www.luketulloch.com/membership @_luketulloch

Join Jeffrey Leitzinger as he discusses statistical significance in antitrust litigation. His podcast is based off the article- Statistical Significance and Statistical Error in Antitrust Analysis co-written with Phillip Johnson and Edward Leamer. The article was selected as the 2018 co-winner of the annual Jerry S. Cohen Memorial Fund Writing Award.

Join Emily, Nora, and Nicole for updates on where creativity comes from and problems with p-values. And, at the end of the episode, new host Irena spices things up with the Scoville scale.

John Ioannidis of Stanford University talks with EconTalk host Russ Roberts about his research on the reliability of published research findings. They discuss Ioannidis's recent study on bias in economics research, meta-analysis, the challenge of small sample analysis, and the reliability of statistical significance as a measure of success in empirical research.

John Ioannidis of Stanford University talks with EconTalk host Russ Roberts about his research on the reliability of published research findings. They discuss Ioannidis's recent study on bias in economics research, meta-analysis, the challenge of small sample analysis, and the reliability of statistical significance as a measure of success in empirical research.

In this episode we talk about a couple of recent papers that get at the issue of training variance, and why we should not just take the max from a training distribution when reporting results. Sadly, our current focus on performance in leaderboards only exacerbates these issues, and (in my opinion) encourages bad science. Papers: https://www.semanticscholar.org/paper/Reporting-Score-Distributions-Makes-a-Difference-P-Reimers-Gurevych/0eae432f7edacb262f3434ecdb2af707b5b06481 https://www.semanticscholar.org/paper/Deep-Reinforcement-Learning-that-Matters-Henderson-Islam/90dad036ab47d683080c6be63b00415492b48506

Scientists excel at finding patterns, but statistical significance isn't always enough to create policy.

There’s a revolution going on right now in the world of golf statistics. But what is the point of all of it? And isn’t even relevant to the average golfer doesn’t have the benefit of shotlink and new exotic computations like strokes gained. In what ways are the traditional stats potentially misleading?

  While Julie began her career in dentistry as an assistant then hygienist, she took an interesting path through science and research to end up where she is now. Dentistry is late to the party once again – find out how! We live in an evidence based world! Become a life long learner! School is meant to help you learn how to learn. Evidence Based Dentistry for the Dental Hygienist - http://www.quintpub.com/display_detail.php3?psku=B6461#.WORnHBIrJPM https://www.ncbi.nlm.nih.gov/pubmed/     We need diagnostic codes in dentistry! Statistical Significance is so amazingly overrated! https://www.kotterinternational.com/book/our-iceberg-is-melting/ http://www.centerforcommunicatingscience.org/ https://en.wikipedia.org/wiki/Practice-based_research_network https://nationaldentalpbrn.org/about.php http://www.adha.org/resources-docs/Annual_Conference_Schedule.pdf  

  While Julie began her career in dentistry as an assistant then hygienist, she took an interesting path through science and research to end up where she is now. Dentistry is late to the party once again – find out how! We live in an evidence based world! Become a life long learner! School is meant to help you learn how to learn. Evidence Based Dentistry for the Dental Hygienist - http://www.quintpub.com/display_detail.php3?psku=B6461#.WORnHBIrJPM https://www.ncbi.nlm.nih.gov/pubmed/     We need diagnostic codes in dentistry! Statistical Significance is so amazingly overrated! https://www.kotterinternational.com/book/our-iceberg-is-melting/ http://www.centerforcommunicatingscience.org/ https://en.wikipedia.org/wiki/Practice-based_research_network https://nationaldentalpbrn.org/about.php http://www.adha.org/resources-docs/Annual_Conference_Schedule.pdf  

Statistician, blogger, and author Andrew Gelman of Columbia University talks with EconTalk host Russ Roberts about the challenges facing psychologists and economists when using small samples. On the surface, finding statistically significant results in a small sample would seem to be extremely impressive and would make one even more confident that a larger sample would find even stronger evidence. Yet, larger samples often fail to lead to replication. Gelman discusses how this phenomenon is rooted in the incentives built into human nature and the publication process. The conversation closes with a general discussion of the nature of empirical work in the social sciences.

Returning to Chat With Traders for a second time is David Bush—first on episode 23. David began as a discretionary trader, more than 20-years ago, but over time he’s developed into a quant trader. And he’s exceptionally good at what he does; David’s been the first place winner of two (real money) trading competitions in recent years. Last time David was on we spoke fairly extensively about his path as a trader and a high-level overview of his process. This time around we covered plenty of new ground—exploring David’s process in greater depth. Also, I particularly liked David’s comments towards the end about, “Intensity, not time.”

In this episode of the SuperDataScience Podcast, I chat with Marketing Expert Sam Flegal. You will know about the essential roles of data in marketing, learn about statistical significance, and get insights and advices about management and career. If you enjoyed this episode, check out show notes, resources, and more at https://www.superdatascience.com/23

What is statistical significance and why is it so critical to the success of experiments? What are some things that you can do to ensure that you're collecting reliable, true data? In this episode, we break down statistical significance and quality data collection. Plus, an update on the LinkedIn Dark Patterns from our first episode! "Ultimately, you want to be able to confidently say, 'when we do other experiments, this conclusion that we came to is going to stay the same'. Because you don't want to have to keep going back and testing the same assumptions over and over again." — Geoff at 20:47 Email us: Hello@UXandGrowth.com Austin on Twitter: Twitter.com/ustinKnight Geoff on Twitter: Twitter.com/dailydaigle Matt on Twitter: Twitter.com/mattrheault

This week Ruth Alexander looks at the extraordinary case of Andreas Georgiou the head of the Greek statistics agency, Elstat. He is facing criminal charges for what amounts to statistical treason. It is a story that goes to the heart of the Greek debt crisis, that includes extreme office politics, alleged e-mail hacking and a statistician facing at least five years in prison. We speak to Economists Miranda Xafa and Professor Yanis Vourafafkis as well as Syriza MP Dimitris Tsoukalas. Also: do American football players die earlier than their fellow Americans?

Statistical significance revolves around having enough participants to make your findings valid. However, the number of participants necessary can vary widely, depending on what you’re studying and how. Join us for a podcast that will help you understand how to make this determination for your projects.

Statistical significance revolves around having enough participants to make your findings valid. However, the number of participants necessary can vary widely, depending on what you’re studying and how. Join us for a podcast that will help you understand how to make this determination for your projects.