Excel16 -5- Quality Check

Working with large datasets can be quite challenging on its own, but working with large datasets that contain mistakes can ruin your mental health very fast. Here you will find a couple of tips on how to spot common mistakes in a few clicks.

Working with small datasets or tables, it is usually easy to spot a mistake, a typo, a value that is much larger than the rest (potential outlier?), a cell format which is wrong or the result of a formula that is invalid. Here are two obvious examples of mistakes that can […]

1. What is quality check and why is it useful?

in Excel16 -5- Quality Check / MS Excel 2016 (EN)

It is often recommended to start looking at a dataset with a plot, prior to any statistical analysis. A plot can reveal a lot: trend, variability, clusters, subpopulations, distribution. It can also reveal mistakes in the dataset, such as aberrant values. Here is an example where local temperatures have been […]

2. Spotting aberrant data with plots

in Excel16 -5- Quality Check / MS Excel 2016 (EN)

If you wish to check that your dataset has been entered properly in Excel, you may use a couple of tools like Sort & Filter and Conditional Formatting. Here is an example where local temperatures have been retrieved from a weather station at Fløyen, in Bergen for the period 1/1/2017-31/12/2017 and […]

3. Spotting aberrant data using “Sort & Filter” or “Conditional Formatting”

in Excel16 -5- Quality Check / MS Excel 2016 (EN)

Providing that your data set is well organized, with categories/variables placed in columns and observations in rows, you may be able to spot aberrant data points by creating a pivot table. What pivot tables are and what they may be useful for is discussed further here. A pivot table can […]

4. Spotting aberrant data with pivot tables

in Excel16 -5- Quality Check / MS Excel 2016 (EN)

When biology adds up, at last…

When biology adds up, at last…

Excel16 -5- Quality Check

1. What is quality check and why is it useful?

2. Spotting aberrant data with plots

3. Spotting aberrant data using “Sort & Filter” or “Conditional Formatting”

4. Spotting aberrant data with pivot tables