Module 9: Data Analysis

Essential questions 

How to analyze numerical experimental data?

Correlation and causation in experimental research. 

Experimental design is used to test a hypothesis regarding the effect of treatment on experimental outcome, compared to the control group. 

Numeric raw data that are collected in experiment, are transformed, and presented in the way that allows to complete a statistical analysis of the relationships between the independent and dependent variables.

Correlation coefficient is usually calculated to indicate the strength of association between the two variables. The statistical analysis is completed to rule out random events that would contribute to the outcomes:

Causation establishes the cause-effect relationship between the independent and dependent variables and is not simply induced from the correlation between the two. 

Controlled experimental study with randomly selected subjects and proper controls is the most efficient way of establishing causation between the two variables.

Numerical data that fit normal distribution can be analyzed with parametric tests. They are based on the assumption that the samples are equally homogeneous (which is attained by random sampling). Commonly used parametric tests can be found in this video:

Non-parametric statistical tests are used to analyze the data that have no normal distribution due to small sample size or other similar events.

Suggested readings

Ali Z., Bhaskar S. B. (2016) Basic statistical tools in research and data analysis. Indian J Anaesth. 60(9):662-669.

Satake E. B. (2015) Statistical Methods and Reasoning for the Clinical Sciences Evidence-Based Practice. Ist ed. San Diego: Plural Publishing, Inc.