In this module, the key objective is to provide students with a solid understanding of hypothesis testing and the potential errors associated with it before embarking on experimental design. Students will learn how to formulate research questions and hypotheses, and define null and alternative hypotheses.

By the end of this module, students will be able to critically evaluate and develop a clear understanding of the potential errors that may arise during hypothesis testing. They will gain the necessary skills to propose well-designed experiments based on sound statistical principles.

The objective of this module is to equip students with the knowledge and skills to design and implement experiments effectively. Students will explore the concepts of significance, power, and effect size and understand their importance in experimental design. They will learn techniques for determining the appropriate sample size, randomization, and blocking methods to reduce bias and increase the validity of experimental results.

By the end of this module, students will be able to design experiments that are statistically rigorous and yield reliable and meaningful conclusions.

In this module, the focus is on the critical evaluation of experimental data using descriptive statistics. Students will learn how to summarize and interpret data by utilizing measures of central tendency and variability. They will explore various data visualization techniques to effectively communicate and present experimental findings.

The key takeaway from this module is that students will develop the skills to assess the quality and reliability of experimental data, enabling them to draw meaningful insights and make informed decisions based on the results.

The objective of this module is to provide students with a comprehensive understanding of inferential statistics and its role in accepting or rejecting hypotheses. Students will learn about data types, assumptions for commonly-used parametric hypothesis tests, data transformation and the interpretation of p-values.

By the end of this module, students will be able to make informed decisions about accepting or rejecting hypotheses based on statistical evidence and understand the limitations of such conclusions.

This module focuses on isolating the efficacy attributable to the pesticide treatment in agricultural experiments. Students will learn statistical approaches to adjust for responses occurring independently of the treatments at zero dose (untreated control), and accurately estimate treatment effects. They will gain an understanding of how to interpret and report corrected efficacy results, ensuring accurate assessment and comparison of different treatments.

The key takeaway from this module is the ability to distinguish and quantify the true efficacy of a pesticide treatment from other factors that may influence the experimental outcome.

The objective of this module is to equip students with the skills to combine multiple observations from disease progress experiments into a single cumulative value using the Area Under the Disease Progress Curve (AUDPC). Students will learn how to assess disease progression over time and quantify the overall disease impact.

By the end of this module, students will understand the significance of AUDPC in disease management and be able to effectively analyze and interpret disease progress data.

In this module, students will delve into dose-response analysis, enabling them to predict values based on measured data. They will learn how to model dose-response relationships, estimate effective dose levels, and assess toxicity and response relationships.

By the end of this module, students will have the ability to predict and interpret responses to varying doses accurately. This knowledge will be crucial in determining optimal dosage levels and ensuring the effectiveness and safety of treatments.

This module focuses on the concept of synergy and its applications in extending the patent life of mixture partners and defending market position by reducing active ingredient rates. Students will explore statistical approaches to measure and optimize synergy effects, enabling them to maximize the benefits of mixture treatments. By the end of this module, students will understand how to assess and report the advantages of synergistic treatments and make informed decisions to enhance market competitiveness while reducing the rates of active ingredients.

In the field of agrochemical research and development (R&D), statistical rules of thumb are general guidelines or principles that can be used to analyse and interpret data. These rules are based on statistical theory and experience, and they can help researchers to make sense of complex data sets and to draw meaningful conclusions. Statistical rules of thumb are useful because they provide a quick and easy way to evaluate the quality and reliability of data, to identify patterns and trends, and to communicate results to others.

In this course, we will explore the advantages and limitations of these rules, and we will consider the factors that influence their usefulness and applicability. We will also discuss the challenges and considerations that are involved in using statistical rules of thumb in agrochemical R&D, and we will consider the role of these rules in the decision-making process.

The course materials for this program are designed to provide comprehensive resources for participants. Each module includes instructional videos that cover the key concepts and topics. These videos will remain accessible even after the completion of the course, allowing participants to revisit the content for future reference. This ensures that participants can review the material at their own pace and reinforce their understanding of the subject matter.

In addition to the videos, participants will have access to downloadable resources. This includes a downloadable version of the slides used in each module, which can serve as a helpful visual aid for reviewing the content. Furthermore, participants will also have the opportunity to download a PDF copy of the book "GUIDE TO ESSENTIAL BIOSTATISTICS." This comprehensive resource will be available to all participants, providing them with a valuable reference guide that they can consult even after the course has ended. These downloadable resources aim to support participants in their continued learning and application of the course material.