statistical-analysis

Hypothesis testing frames null and alternative hypotheses to evaluate whether a marketing campaign changed average session duration, interpreting p-values and practical significance to guide data-driven decisions across cohorts.

Descriptive statistics summarize data distributions using mean, median, mode, variance, and percentiles. This reveals central tendency and spread to identify the main distribution and interpret results with a concise narrative.

Outlier detection identifies anomalies in your dataset and evaluates data quality. This process assesses the robustness of statistical conclusions by examining how extreme values impact overall variability and significance.

Yes, trend analysis identifies patterns over time using Python-based tools like pandas and numpy. It provides guidance for simple forecasting approaches to help understand directional shifts in business metrics or scientific results.

Yes, the workflow requires Python-based tools such as pandas, numpy, and scipy to compute statistics and test hypotheses. These libraries process customer data and scientific results to calculate central tendency and significance.