Advanced Topic III: Further Remarks

2010 ◽  
pp. 305-364
Keyword(s):  
Advanced Topic

Knowing What You Don’t Know

2019 ◽  
Author(s):  
Angela Duckworth ◽  
Keyword(s):  
Critical Thinking ◽  
The Other ◽  
Character Strength ◽  
Intellectual Humility ◽  
Over Time ◽  
Advanced Topic

The other day, I attended a lecture by a world-renowned professor on an advanced topic in statistics. There were all kinds of graphs and equations, and the thesis he was advancing was only accessible to people who already knew a lot about how to work with data. But that wasn't what impressed me the most. Instead, it was something this professor said a half-dozen times in the course of an hour. I don't know. Of course, there was a lot he did know, and he had a ready reply in response to most questions. But when he didn't know the answer, he said so. In other words, what impressed me most was not his intellectual prowess but his intellectual humility—a strength of character I admit I need to practice. It turns out that intellectually humble people are more curious, open to new experiences, and tolerant of ambiguity. They're less dogmatic and less prone to judge others based on their religious opinions. They seek out views that differ from their own. A lot of what schools call “critical thinking” comes down to intellectual humility: knowing what you don't know. And like any other character strength, intellectual humility develops with practice and encouragement. Have you ever given a presentation and hoped and prayed that nobody would ask you a question for which you weren't prepared? I once felt that way; I didn't want to look stupid. But over time, fear of what I don't know has been replaced with excitement about what I have yet to learn.

Analysis of covariance in agronomy and crop research

2011 ◽  
Vol 91 (4) ◽  
pp. 621-641 ◽  
Author(s):  
Rong-Cai Yang ◽  
Patricia Juskiw
Keyword(s):  
Regression Analysis ◽  
Spatial Variation ◽  
Mixed Models ◽  
Mixed Model ◽  
Statistical Technique ◽  
Analysis Of Covariance ◽  
Statistical Software ◽  
Mixed Model Analysis ◽  
Crop Research ◽  
Advanced Topic

Yang, R.-C. and Juskiw, P. 2011. Analysis of covariance in agronomy and crop research. Can. J. Plant Sci. 91: 621–641. Analysis of covariance (ANCOVA) is a statistical technique that combines the methods of the analysis of variance (ANOVA) and regression analysis. However, ANCOVA is an advanced topic that often appears towards the end of many textbooks, and thus, it is either taught cursorily or ignored completely in many statistics classes. Additionally, many elaborated applications of ANCOVA to agronomy and crop research along with uses of the latest statistical software are rarely described in textbooks or classes. The objectives of this paper are to provide an overview on conventional ANCOVA and to introduce more advanced uses of ANCOVA under mixed models. We describe three elaborate applications including (i) the use of ANCOVA for dissecting dosage responses for different treatments, (ii) stability of treatments across multiple environments and (iii) removal of spatial variation that is not effectively controlled by blocking. These analyses illustrate that ANCOVA is either a simpler analysis or provides more information than conventional statistical methods. We provide a technical appendix ( Appendix A ) on principles and theory underlying mixed-model analysis of ANCOVA along with SAS programs ( Appendix B ) for more uses and in-depth understanding of this powerful technique in agronomy and crop research.

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