BioScience Solutions

LEAN INNOVATION in CROP PROTECTION R&D II: FRONTLOADING and VISUAL PLANNING

teicher@biocomm.eu — Fri, 28 May 2021 18:12:16 +0000

In the previous article, we considered how frontloading can facilitate the introduction of business-driven objectives and associated planning processes early in the project life cycle, when the ability to carry out changes is high. While frontloading adds time and expense to the initial stages of a project, these costs are minor compared to the costs […]

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LEAN INNOVATION in CROP PROTECTION R&D I: targeting customer value.

teicher@biocomm.eu — Fri, 28 May 2021 18:06:03 +0000

“The greatest danger for most of us is not that our aim is too high, and we miss it, but that it is too low, and we reach it.” Michelangelo. In today’s competitive crop protection world, the rapidly changing strategic situation requires organizations to demonstrate strategic agility to transform strategies into action. Within the R&D […]

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Biologicals in Crop Protection – Challenges and Opportunities – are they a better option than chemicals from a human health perspective?

teicher@biocomm.eu — Thu, 27 May 2021 17:42:40 +0000

In this twelfth article of a series on BioPesticides, I write on Biopesticides from a health and environmental perspective. If this article provides value, please consider liking and sharing. Your feedback is invaluable, and helps new readers discover my work! In recent years any discussion of crop protection issues has inevitably led to the question: […]

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Five Top Trends for Biological Pesticides in 2020

teicher@biocomm.eu — Mon, 27 Jan 2020 18:27:41 +0000

The dawn of a new decade is a great opportunity to evaluate the evolving trends and technologies which are giving biological pesticides an increasingly competitive edge. Here are five to watch in 2020: 1. Biologicals market growth will continue to outpace conventional pesticides Estimates of global market sizes for crop protection products (conventional pesticides as […]

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Guide to Essential Biostatistics XXI: A Short History of Applied BioStatistics

teicher@biocomm.eu — Wed, 17 Apr 2019 10:01:33 +0000

In the previous articles in this series, we explored the Scientific Method, Proposing Hypotheses and Type-I and Type-II errors, Designing and implementing experiments (Significance, Power, Effect, Variance, Replication, Experimental Degrees of Freedom and Randomization), Critically evaluating experimental data (Q-test; SD, SE, and 95%CI) as well as Two-Sample Means Comparisons (the t-test) and ANOVA. The first quarter of the 20th century was an intense period of […]

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Guide to Essential Biostatistics XX: Chi-square test: goodness of fit for sample validation

teicher@biocomm.eu — Wed, 10 Apr 2019 09:52:11 +0000

In the previous articles in this series, we explored the Scientific Method, Proposing Hypotheses and Type-I and Type-II errors, Designing and implementing experiments (Significance, Power, Effect, Variance, Replication, Experimental Degrees of Freedom and Randomization), Critically evaluating experimental data (Q-test; SD, SE, and 95%CI) as well as Two-Sample Means Comparisons (the t-test) and ANOVA. The chi-squared distribution or χ2-distribution, developed by English mathematician and statistician Karl […]

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Guide to Essential Biostatistics XIX: Linear regression (PROBIT)

teicher@biocomm.eu — Wed, 03 Apr 2019 09:38:58 +0000

In the previous articles in this series, we explored the Scientific Method, Proposing Hypotheses and Type-I and Type-II errors, Designing and implementing experiments (Significance, Power, Effect, Variance, Replication, Experimental Degrees of Freedom and Randomization), Critically evaluating experimental data (Q-test; SD, SE, and 95%CI) as well as Two-Sample Means Comparisons (the t-test) and ANOVA. Probit (“probability unit”) models were developed by American biologist and statistician Chester […]

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Guide to Essential Biostatistics XVIII: Non-linear regression (Sigmoidal dose-responses

teicher@biocomm.eu — Wed, 27 Mar 2019 10:35:40 +0000

In the previous articles in this series, we explored the Scientific Method, Proposing Hypotheses and Type-I and Type-II errors, Designing and implementing experiments (Significance, Power, Effect, Variance, Replication, Experimental Degrees of Freedom and Randomization), Critically evaluating experimental data (Q-test; SD, SE, and 95%CI) as well as Two-Sample Means Comparisons (the t-test) and ANOVA. Bioassays (analytical methods to determine potency of a substance by its effect […]

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Guide to Essential Biostatistics XVII: Inferential Statistics – post ANOVA tests

teicher@biocomm.eu — Wed, 06 Mar 2019 13:45:24 +0000

In the previous articles in this series, we explored the Scientific Method, Proposing Hypotheses and Type-I and Type-II errors, Designing and implementing experiments (Significance, Power, Effect, Variance, Replication, Experimental Degrees of Freedom and Randomization), Critically evaluating experimental data (Q-test; SD, SE, and 95%CI) as well as Two-Sample Means Comparisons (the t-test). Post-ANOVA tests are typically performed after the f-ratio has indicated […]

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Guide to Essential Biostatistics XVI: Inferential Statistics – ANOVA

teicher@biocomm.eu — Wed, 27 Feb 2019 13:49:26 +0000

In the previous articles in this series, we explored the Scientific Method, Proposing Hypotheses and Type-I and Type-II errors, Designing and implementing experiments (Significance, Power, Effect, Variance, Replication, Experimental Degrees of Freedom and Randomization), Critically evaluating experimental data (Q-test; SD, SE, and 95%CI) as well as Two-Sample Means Comparisons (the t-test). While the t-test is used to compare the means […]

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