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Introduction to Descriptive Statistics
Why Descriptive Statistics?
Before diving into advanced analytics or machine learning, we first need to understand the shape and story of our data. Descriptive statistics help us:
Summarise large datasets into understandable insights
Detect outliers or unusual patterns
Communicate findings clearly to decision-makers
Professionals conducting in-depth statistical analysis on their laptops, surrounded by a virtual overlay of graphs and data visualisations.
Measures of Central Tendency
At the heart of descriptive statistics are the measures of central tendency. These tell us the “typical” value in a dataset.
Mean (Average): Add all values and divide by the number of values. Great for normally distributed data, but sensitive to outliers.
Median (Middle Value): The point where half the data lies above and half below. More reliable when data is skewed or has extreme values.
Mode (Most Frequent Value): Best for categorical data (e.g., most purchased product).
Why They Matter
In business, knowing whether to use mean or median can completely change your conclusions.
The mode is perfect for identifying customer preferences or common behaviours.
Together, these measures give a balanced, nuanced picture of data.
Visualising Central Tendency
Histograms: Show how mean/median/mode sit within a distribution.
Boxplots: Highlight the median, spread, and outliers.
Bar Charts: Useful for spotting the mode in categorical data.
What’s Next?
This free resource is just the beginning. In the next blog, we’ll dive deeper with a retail case study, exploring:
Different types of means (arithmetic, geometric, harmonic, trimmed)
How skewed distributions change the story your data tells
Practical interpretations with real charts and examples
Stay tuned- we’ll move from the basics into real-world analytics that help businesses and researchers make smarter decisions.
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