Normal Distribution
A symmetric, bell-shaped probability distribution in which values cluster around the mean, with predictable proportions within each standard deviation.
The normal distribution is a continuous, bell-shaped distribution defined by its mean and standard deviation. It is symmetric, so the mean, median, and mode coincide, and it follows a known rule: about 68 percent of values lie within one standard deviation of the mean, 95 percent within two, and 99.7 percent within three. Many natural and social phenomena approximate it, and sampling distributions of means tend toward normality as samples grow larger.
Normality matters in a thesis because many common tests, including t-tests, ANOVA, and standard regression, assume it in some form. Check your variables with histograms and skewness and kurtosis statistics, report what you found, and switch to non-parametric tests or transform the data when the assumption clearly fails. Documenting these checks strengthens both your methodology chapter and your results chapter.
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