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Experimental design for laboratory biologists : Maximising information and improving reproducibility / Stanley E. Lazic.

By: Material type: TextTextLanguage: English Description: xv, 412 pages : illustrations ; 25 cmISBN:
  • 9781107074293 (hbk)
  • 9781107424883 (pbk)
Subject(s): DDC classification:
  • 570.724 23 LAZ
Contents:
1. Introduction 2. Key ideas in experimental design 3. Replication (what is'N'?) 4. Analysis of common designs 5. Planning for success 6. Exploratory data analysis.
Summary: Specifically intended for lab-based biomedical researchers, this practical guide shows how to design experiments that are reproducible, with low bias, high precision, and widely applicable results. With specific examples from research using both cell cultures and model organisms, it explores key ideas in experimental design, assesses common designs, and shows how to plan a successful experiment. It demonstrates how to control biological and technical factors that can introduce bias or add noise, and covers rarely discussed topics such as graphical data exploration, choosing outcome variables, data quality control checks, and data pre-processing. It also shows how to use R for analysis, and is designed for those with no prior experience.
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Item type Current library Collection Call number Status Date due Barcode
General Books General Books CUTN Central Library Sciences Non-fiction 570.724 LAZ (Browse shelf(Opens below)) Available 41010


1. Introduction 2. Key ideas in experimental design 3. Replication (what is'N'?) 4. Analysis of common designs 5. Planning for success 6. Exploratory data analysis.

Specifically intended for lab-based biomedical researchers, this practical guide shows how to design experiments that are reproducible, with low bias, high precision, and widely applicable results. With specific examples from research using both cell cultures and model organisms, it explores key ideas in experimental design, assesses common designs, and shows how to plan a successful experiment. It demonstrates how to control biological and technical factors that can introduce bias or add noise, and covers rarely discussed topics such as graphical data exploration, choosing outcome variables, data quality control checks, and data pre-processing. It also shows how to use R for analysis, and is designed for those with no prior experience.

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