Are my data normal?

Area of Science:

• Statistical analysis
• Data science
• Scientific methodology

Background:

• Many statistical inferences rely on the assumption of normally distributed data.
• This assumption is often untested, potentially leading to incorrect conclusions.
• Understanding data distribution is crucial for valid scientific interpretation.

Purpose of the Study:

• To evaluate the prevalence and validity of the normal distribution assumption in statistical analysis.
• To discuss the likelihood of experimental data conforming to a normal distribution.
• To present methods for testing whether a data sample originates from a normally distributed population.

Main Methods:

• Review of common statistical practices and assumptions.
• Discussion of scenarios where the normal distribution is typically assumed.
• Exploration of statistical tests for normality.

Main Results:

• The normal distribution assumption is frequently applied without verification.
• Data may not always conform to a normal distribution, compromising inference.
• Various statistical tests can assess the normality of a data sample.

Conclusions:

• The validity of the normal distribution assumption should be critically assessed.
• Employing normality tests is essential for robust statistical inference.
• Ensuring data normality enhances the reliability of scientific findings.