Experimental Design 1

Question 1

Define inductive reasoning and give an example:

Answer 1

The process of inferring a general law or principle from observation of a particular instance
Example -

Question 2

Define deductive reasoning and give an example :

Answer 2

Inference by reasoning from a general principle to a particular situation (applying a principle)

Question 3

What is the process of the scientific method?

Answer 3

1 - Question
2 - Hypothesis
3 - Experiment
4 - Analyse data
5 - Draw conclusions
6 - Impact of Data; Model may be strengthened or modified

Question 4

Define experimental control:

Answer 4

Techniques and procedures used in an experiment to ensure that the effects observed are due to the manipulation of the independent variable, rather than other external factors or variables

Question 5

What are some obstacles that need to be overcome in experimental control?

Answer 5

Perfectly definite change and do nothing else to influence result
Biologic variability
Chance; experimental errors, faults in methods, differences in reagents
Bias in experimenter and experimental design

Question 6

Explain the role of inductive and deductive reasoning in Hypothesis Generation and Experimental Design:

Answer 6

Inductive reasoning is essential in the exploratory phase of research, where scientists gather observations based on data + trends and use them to formulate general hypotheses
Deductive reasoning is essential for testing hypotheses and designing experiments that provide clear, testable predictions based on established theories

Question 7

Explain those features of the scientific Literature that differentiate it from other forms of reading material:

Answer 7

Undergo peer review process
Based on empirical evidence data and observations collected through experiments
Structured format - IMRaD
Citation of sources providing credibility
Purpose of advancing knowledge
Neutral and objective tone

Question 8

Explain the need for Replication in experimental procedures

Answer 8

Verifying findings and ensuring they are not due to chance or error.
Building confidence in the robustness of scientific conclusions.
Identifying potential biases or errors in experimental design or execution.
Testing generalizability of results across different conditions.
Establishing scientific consensus and advancing knowledge.
Preventing false positives and negatives by providing a safety check on results.
Refining methods for more accurate and reliable outcomes.

Question 9

Discuss the need for statistical approaches in experimental design, analysis and interpretation:

Answer 9

In experimental design;
Ensure that experiments are planned in a way that minimises bias, controls variables, and collects meaningful data.

In data analysis
Provide methods for summarising, modelling, and inferring from the data, allowing for sound conclusions about relationships between variables.

In interpretation:
Statistics offer tools for assessing the significance, relevance, and limitations of findings, avoiding over-interpretation and false conclusions.