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What do you do if logical reasoning fails in data analysis and interpretation in biotechnology?

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Data analysis and interpretation are essential skills in biotechnology, where you often deal with complex and noisy data sets from various sources. Logical reasoning is the ability to apply rational and systematic thinking to evaluate evidence, draw conclusions, and solve problems. However, logical reasoning is not always sufficient or reliable when dealing with data in biotechnology. Sometimes, you may encounter situations where your logic fails to explain the data, or where the data contradicts your logic. What do you do in such cases? In this article, we will explore some possible causes and solutions for logical reasoning failures in data analysis and interpretation in biotechnology.

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Matej Krajcovic

AI for Bio/Chem, Big Pharma, PhD @ Cornell
Pouya Behrouzi

CEO at Rayca Precision | AI Drug Discovery | Target Validation | Generative Biology | Life Sciences Entrepreneur

1 Data quality issues

One of the most common reasons why logical reasoning fails in data analysis and interpretation is that the data itself is faulty or incomplete. Data quality issues can arise from various sources, such as human errors, measurement errors, sampling errors, missing values, outliers, noise, bias, or inconsistency. Data quality issues can affect the validity, reliability, and accuracy of your data, and thus compromise your logical reasoning. To avoid or minimize data quality issues, you should always check and validate your data before analysis, and use appropriate methods and tools to clean, preprocess, and transform your data.

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Matej Krajcovic

AI for Bio/Chem, Big Pharma, PhD @ Cornell
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Quality controls for laboratory equipment are essential in order to obtain high quality data. It is critical to follow testing procedures recommended by the manufacturer and also collect data on these QC standards to see how they trend over time. Once years of data are collected, then the lab personnel will have a better feel on when things usually go wrong (e.g. during holiday break when the machine is not being used for an extended period of time) and adjust the maintenance accordingly to proactively prevent issues.

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Cynthia Ramos Barboza

Ingeniera Industrial | Logística | Control de Calidad | Auditoría | Procesos | ISO FSSC22000 | HACCP
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Para superar o reducir los problemas de complejidad de los datos, siempre debe explorar y visualizar los datos antes de analizarlos, y utilizar los métodos y herramientas adecuados para reducir, seleccionar y modelar los datos.

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2 Data complexity issues

Another reason why logical reasoning fails in data analysis and interpretation is that the data is too complex or heterogeneous to be analyzed by simple or linear methods. Data complexity issues can arise from various sources, such as high dimensionality, multicollinearity, nonlinearity, interactions, confounding factors, or hidden patterns. Data complexity issues can affect the simplicity, clarity, and generalizability of your data, and thus challenge your logical reasoning. To overcome or reduce data complexity issues, you should always explore and visualize your data before analysis, and use appropriate methods and tools to reduce, select, and model your data.

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Matej Krajcovic

AI for Bio/Chem, Big Pharma, PhD @ Cornell
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I would not say that the data has complexity issues. The data are the data. If the data were collected in an appropriate manner and are accurate and valid then the data don't have any issues. The issue is with the analysis which might be inadequate. Other way to overcome this is to filter the data based on some logical criteria to get a more homogeneous dataset that your methods are capable of analyzing.

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Pouya Behrouzi

CEO at Rayca Precision | AI Drug Discovery | Target Validation | Generative Biology | Life Sciences Entrepreneur
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Sometimes logical reasoning hits a snag. Take data complexity, for instance. It's like trying to untangle a web of intricacies that just don't want to cooperate with simple linear methods. The thing is, data complexity can rear its head in all sorts of ways—whether it's high dimensionality, multicollinearity, or those pesky confounding factors. It's like trying to make sense of a puzzle with missing pieces and extra ones thrown in for good measure. One trick is to dive deep into your data, really get to know its ins and outs before you even think about crunching numbers.

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Cynthia Ramos Barboza

Ingeniera Industrial | Logística | Control de Calidad | Auditoría | Procesos | ISO FSSC22000 | HACCP
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Las oportunidades de aprendizaje pueden afectar al crecimiento, el desarrollo y el rendimiento de los datos y, por lo tanto, mejorar el razonamiento lógico. Para aprovechar o generar oportunidades de aprendizaje, siempre debe buscar y apreciar las aportaciones, el apoyo y la diversidad de los demás, y utilizar métodos y herramientas adecuados para aprender, experimentar y crear con sus datos.

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3 Logical fallacies

A third reason why logical reasoning fails in data analysis and interpretation is that you commit logical fallacies, which are errors or flaws in reasoning that undermine the logic of your arguments. Logical fallacies can arise from various sources, such as cognitive biases, emotional influences, rhetorical tricks, or false assumptions. Logical fallacies can affect the soundness, coherence, and persuasiveness of your data, and thus impair your logical reasoning. To avoid or correct logical fallacies, you should always be aware and critical of your own and others' reasoning, and use appropriate methods and tools to test, verify, and refute your data.

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Cynthia Ramos Barboza

Ingeniera Industrial | Logística | Control de Calidad | Auditoría | Procesos | ISO FSSC22000 | HACCP
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los datos son demasiado complejos o heterogéneos para ser analizados por métodos simples o lineales. Los problemas de complejidad de los datos pueden surgir de varias fuentes, como la alta dimensionalidad, la multicolinealidad, la no linealidad, las interacciones, los factores de confusión o los patrones ocultos

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4 Alternative explanations

A fourth reason why logical reasoning fails in data analysis and interpretation is that you overlook or ignore alternative explanations, which are plausible or competing hypotheses that can account for the same data. Alternative explanations can arise from various sources, such as different perspectives, frameworks, theories, or paradigms. Alternative explanations can affect the uniqueness, specificity, and relevance of your data, and thus weaken your logical reasoning. To acknowledge or eliminate alternative explanations, you should always be open and curious about other possibilities, and use appropriate methods and tools to compare, contrast, and integrate your data.

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Pouya Behrouzi

CEO at Rayca Precision | AI Drug Discovery | Target Validation | Generative Biology | Life Sciences Entrepreneur
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In my years of diving deep into data analysis in biotech, I've seen my fair share of moments where logical reasoning hits a roadblock. Now, these aren't just your run-of-the-mill mistakes. We're talking about errors or flaws in reasoning that can throw a wrench in the whole operation. They can sneak in from all sorts of places such as cognitive biases, emotions running high, even those sneaky rhetorical tricks. Be critical of your own thinking and don't hesitate to question others' reasoning too. It's all about using the right methods and tools to put your data through the wringer, making sure it holds up under scrutiny.

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5 Ethical issues

A fifth reason why logical reasoning fails in data analysis and interpretation is that you encounter or create ethical issues, which are moral or social dilemmas that arise from the use or misuse of data. Ethical issues can arise from various sources, such as privacy, confidentiality, consent, ownership, accountability, or responsibility. Ethical issues can affect the integrity, trustworthiness, and value of your data, and thus compromise your logical reasoning. To avoid or resolve ethical issues, you should always follow and respect the ethical principles, standards, and guidelines that apply to your data, and use appropriate methods and tools to protect, anonymize, and share your data.

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6 Learning opportunities

A sixth reason why logical reasoning fails in data analysis and interpretation is that you face or create learning opportunities, which are chances or challenges to improve your knowledge, skills, or abilities in data analysis and interpretation. Learning opportunities can arise from various sources, such as feedback, criticism, collaboration, or innovation. Learning opportunities can affect the growth, development, and performance of your data, and thus enhance your logical reasoning. To embrace or generate learning opportunities, you should always seek and appreciate the input, support, and diversity of others, and use appropriate methods and tools to learn, experiment, and create with your data.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What do you do if logical reasoning fails in data analysis and interpretation in biotechnology?

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1 Data quality issues

2 Data complexity issues

3 Logical fallacies

4 Alternative explanations

5 Ethical issues

6 Learning opportunities

7 Here’s what else to consider

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