Guide to Essential BioStatistics IV: Statistical Significance, Power and Effect Size.

In this fourth article in the LabCoat Guide to BioStatistics series, intended as a basic refresher for scientists and technicians, we learn about Statistical Significance, Power and Effect Size. The 5% significance level (a threshold arbitrarily decided on by biologists) ensures we are 95% confident that we will not make a Type-I error (identifying false positives). The power or sensitivity of a test is used to determine the appropriate sample size for a test or experiment. For crop protection researchers, effect size (or treatment effect) is often defined as the minimum improvement in efficacy needed to justify the costs of developing a new pesticide formulation. #statistics #biostatistics #experimentalresearch #cropprotection #bioscience #GuideToEssentialBiostatistics #Experimentaldesign
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GUIDE TO ESSENTIAL BIOSTATISTICS: AGCHEM & BIOSCIENCE (THE LABCOAT GUIDE TO CROP PROTECTION Book 3)

GUIDE TO ESSENTIAL BIOSTATISTICS is an easily accessible primer for scientists and research workers not trained in mathematical theory, and provides a readily accessible overview on how to plan, implement and analyze experiments without access to a dedicated staff of statisticians. #GuideToEssentialBiostatistics #Cropprotection #Experimentaldesign #Biostatistics #statistics
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Guide to Essential BioStatistics III: Type I and Type II errors

In this third article in the LabCoat Guide to BioStatistics series, intended as a basic refresher for scientists and technicians, we learn about Type I and Type II errors. A Type-I error would lead us to reject the Null hypothesis, claiming that there IS a difference between treatments (false positive) when there is none. A Type-II error would lead us to not reject the Null hypothesis, erroneously concluding that there is NOT a difference in phytotoxicity between the treatments. #statistics #biostatistics #experimentalresearch #cropprotection #bioscience #GuideToEssentialBiostatistics #Experimentaldesign
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Guide to Essential BioStatistics II: Proposing a Hypothesis (the Null Hypothesis)

In this second article in the LabCoat Guide to BioStatistics series, intended as a basic refresher for scientists and technicians, we learn how to propose a hypothesis. A hypothesis is the basic tenet of the scientific method. It is often described as an “educated guess” to account for an observed phenomenon or for a research idea, and is based on prior knowledge and observation. A Research or Working Hypothesis is a proposal or prediction to be proved or disproved through experimentation or observation. #statistics #biostatistics #experimentalresearch #cropprotection #bioscience #GuideToEssentialBiostatistics #Experimentaldesign
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Guide to Essential BioStatistics I: The Scientific Method

In this first article in the LabCoat Guide to BioStatistics series, intended as a basic refresher for scientists and technicians, we cover essential principles of the Scientific Method. The Scientific Method is the mainstay of R&D and provides an objective and systematic approach to experimentation, by minimizing the influence of experimenter bias or prejudice in the form of preconceived notions. #statistics #biostatistics #experimentalresearch #cropprotection #bioscience #GuideToEssentialBiostatistics #Experimentaldesign
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AgBioScience and Crop Protection News Aggregator

FREE RESOURCE!! BioScience News Aggregator Don’t have the time to spend a significant amount of your day following AgChem and Bioscience Industry & R&D updates? I didn’t either – so I set up a news aggregator to compile a daily update, and made it available completely free! #sustainability #bioscience #cropprotection #resources #consulting #agrochemicals #biologicalcontrol #marketingstrategy #researchdevelopment #consultation #blueoceanstrategy #businessdevelopment
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Crop Protection Strategic R&D Management Consulting

Are you starting a new project, moving teams or starting a new role in the #cropprotection industry? The LABCOAT GUIDE TO ESSENTIAL BIOSTATISTICS is an easily accessible primer for scientists and research workers not trained in mathematical theory, and provides a readily accessible overview on how to plan, implement and analyze experiments without access to a dedicated staff of statisticians. #GuideToEssentialBiostatistics #Cropprotection #Experimentaldesign #Biostatistics #statistics
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Crop Protection Strategic R&D Management Consulting

Are you starting a new project, moving teams or starting a new role in the #cropprotection industry? The LABCOAT GUIDE TO CROP PROTECTION aims to give you a complete introduction to Pesticide and Biopesticide Mode Of Action and Formulation, Strategic R&D Management and Essential BioStatistics! #sustainability #bioscience #cropprotection #resources #consulting #agrochemicals #biologicalcontrol #marketingstrategy #researchdevelopment #consultation #blueoceanstrategy #businessdevelopment
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Guide to Essential Biostatistics XXII: Demonstrating synergy from efficacy data - the Additive Dose Model (Wadley/Isobole method)

Active ingredient mixes can extend the patent life of mixture partners and thus defend the market position. A synergistic effect is defined as whenever the action of a combination of active ingredients is greater than the sum of the actions of the individual components. Not only is synergism an accepted argument against any allegation of obviousness in patent applications, but active ingredient synergism also allows better pest, pathogen or weed control with equal or reduced active ingredient rates. However, there is a risk that synergism could cause injury to crops or other non-target plants. In crop protection, two reference models are commonly used to demonstrate synergy: the Multiplicative Survival Model (MSM) for mixtures with differing action, and the Additive Dose Model (ADM) for mixtures with similar action. #cropprotection #synergy #statistics #biostatistics #reference
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Guide to Essential Biostatistics XXII: Demonstrating synergy from efficacy data - the Multiplicative Survival Model (Colby’s method)

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