Science And Scientific Thinking at University Of Dublin, Trinity College | Flashcards & Summaries

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Theoretical vs Experimental 
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A theory is expected to explain existing experimental results and to predict new results.
An experiment is usually expected to check the validity of existing theories and to gather data for modifying them.
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Qualitative/ Conceptual vs Quantitative 
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Quantitative research deals with numbers and statistics (measuring variables and testing hypotheses). 
Qualitative research deals with words and their meanings (exploring concepts and experiences in more detail)
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Example of a control group 
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The researcher tests whether or not a new fertiliser has an effect on plant growth. The negative control group would be the set of plants grown without the fertiliser, but under the exact same conditions as the experimental group
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Importance of cross validation and multi modal research 
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Answering the same question with different approaches and methods and arriving at the same result makes the scientific enquiry very strong! 
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Logical fallacy 
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TESTE DEIN WISSEN
An error in reasoning that weakens or invalidates an argument 
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Cognitive bias 
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TESTE DEIN WISSEN
Systematic errors in a person’s subjective way of thinking 
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Correlation vs Causation 
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Correlation tests for a relationship between two variables. However, seeing two variables moving together for not necessarily mean one variable causes the other to occur 
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Noise 
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Unwanted components for a signal 
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Artefact 
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Unwanted physiological effects recorded along with the desired signal/data 
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Artefact vs noise 
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Artefact is more specific 
Noise is less structured 
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Contexts of bias in data 
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Bias can be the influences due to pre-existing or unwanted factors that may undesirably shift the decision metrics towards a specific side. Bias can also be referred to as a constant unwanted component added to all the data points, but also to the non zero estimation error that depends on how many trials you average for your estimation  
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Interpolation va Extrapolation 
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Interpolation is predicting values that fall within the range of data points taken
Extrapolation is predicting values for outside the data points the range of data taken 
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Beispielhafte Karteikarten für deinen Science and Scientific Thinking Kurs an der University of Dublin, Trinity College - von Kommilitonen auf StudySmarter erstellt!

Q:
Theoretical vs Experimental 
A:
A theory is expected to explain existing experimental results and to predict new results.
An experiment is usually expected to check the validity of existing theories and to gather data for modifying them.
Q:
Qualitative/ Conceptual vs Quantitative 
A:
Quantitative research deals with numbers and statistics (measuring variables and testing hypotheses). 
Qualitative research deals with words and their meanings (exploring concepts and experiences in more detail)
Q:
Example of a control group 
A:
The researcher tests whether or not a new fertiliser has an effect on plant growth. The negative control group would be the set of plants grown without the fertiliser, but under the exact same conditions as the experimental group
Q:
Importance of cross validation and multi modal research 
A:
Answering the same question with different approaches and methods and arriving at the same result makes the scientific enquiry very strong! 
Q:
Logical fallacy 
A:
An error in reasoning that weakens or invalidates an argument 
Mehr Karteikarten anzeigen
Q:
Cognitive bias 
A:
Systematic errors in a person’s subjective way of thinking 
Q:
Correlation vs Causation 
A:
Correlation tests for a relationship between two variables. However, seeing two variables moving together for not necessarily mean one variable causes the other to occur 
Q:
Noise 
A:
Unwanted components for a signal 
Q:
Artefact 
A:
Unwanted physiological effects recorded along with the desired signal/data 
Q:
Artefact vs noise 
A:
Artefact is more specific 
Noise is less structured 
Q:
Contexts of bias in data 
A:
Bias can be the influences due to pre-existing or unwanted factors that may undesirably shift the decision metrics towards a specific side. Bias can also be referred to as a constant unwanted component added to all the data points, but also to the non zero estimation error that depends on how many trials you average for your estimation  
Q:
Interpolation va Extrapolation 
A:
Interpolation is predicting values that fall within the range of data points taken
Extrapolation is predicting values for outside the data points the range of data taken 
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