statistical-analysis

To test a marketing campaign's statistical significance, apply hypothesis testing methods like t-tests or chi-squared tests to your sales datasets. This determines whether observed differences in revenue are statistically significant rather than just random variation.

To identify outliers in time-series sales data, apply outlier detection methods using Z-scores or IQR. This helps you detect unusual data points like unexpected revenue spikes or dips and provides methods for handling these anomalies.

To calculate descriptive statistics for summarizing a dataset, compute measures of central tendency including mean, median, and mode alongside spread metrics like standard deviation and IQR. This provides a concise summary of your data distribution.

Yes, you can use moving averages to smooth time-series data for trend analysis. This allows you to compare performance over different periods such as WoW, MoM, and YoY, helping you identify underlying seasonal trends in metrics like monthly sales.

Yes, running hypothesis testing and interpreting distributions requires an understanding of statistical concepts and common tests. This foundational knowledge is necessary to properly assess statistical significance and evaluate statistical claims within your datasets.