Statistics Stumbles: 10 Common Mistakes in Data Analysis Training

In this weeks' newsletter, I’d like to shed some light on the top 10 mistakes that often derail the training and development of data analysis skills.

So, grab your notebook and let's uncover the pitfalls together!

1.?Misunderstanding Measures of Central Tendency

Misinterpreting measures like mean, median, and mode can lead to skewed perceptions of the data's central values, resulting in flawed analysis and decision-making.

2.?Ignoring Measures of Dispersion

Neglecting measures of dispersion, such as standard deviation and variance, can obscure important insights into the variability and spread of the data, leading to incomplete analyses.

3.?Misusing Percentiles and Quartiles

Improper application of percentiles and quartiles can distort the understanding of data distribution and lead to inaccurate assessments of position and variability within the dataset.

4.?Confusing Correlation with Causation

Failing to recognise the difference between correlation and causation can result in erroneous conclusions and misguided strategies based on spurious relationships within the data. I see people misunderstand this aspect a lot.

5.?Overlooking Skewness and Kurtosis

Neglecting to assess skewness and kurtosis can hinder the understanding of data distribution shape and lead to inappropriate statistical modelling and analysis. We’re not all normal…ly distributed.

To find out the rest of the common mistakes in data analysis training, head over to our blog!

Have a great week!