statistical-analysis

To calculate descriptive statistics, you compute central tendency using mean, median, or mode based on your data distribution. You also measure spread using standard deviation, IQR, and percentiles to provide context for business analytics decisions.

Performing hypothesis testing involves outlining null and alternative hypotheses, calculating p-values, and determining confidence intervals. You must evaluate both statistical and practical significance to draw valid inferences from your experimentation data.

The best way to detect outliers in numeric data is by analyzing spread and percentiles. Using standard deviation and interquartile range (IQR) helps identify anomalies and robustly handle data distributions for quality control.

Yes, you can use trend analysis to detect seasonal patterns in business analytics. This process identifies trends over time and provides simple forecasts, complete with uncertainty notes to guide research and decision-making.

You should use the median instead of the mean for central tendency when your data distribution is skewed or contains extreme outliers. The median provides a more robust measure of central tendency for such numeric data.

A key limitation of p-values is that they only indicate statistical significance, not practical significance. You must interpret confidence intervals and effect sizes alongside p-values to avoid making cautious statistical claims.