Based on your request, the phrase likely refers to technical parameters within a data-driven or software context, specifically relating to how misleading graphs and visualizations are constructed.
Inappropriate Data Visualization often involves manipulated axes, missing baselines, or cherry-picked data to create a false impression of trends.
Examples of Inappropriate Data include vertical scales that do not start at zero (exaggerating differences) or inconsistent scales, such as having a first interval of 20 followed by 5.
Misleading Graphs are frequently used to distort trends by cutting off the y-axis, causing minor changes to appear as significant spikes.
This video explains how misleading graphs can be used to distort data:
False Trends are also created by presenting incomplete data (e.g., showing only part of a year’s data) or using incorrect chart types, such as pie charts that do not add up to 100%. To help you with this topic, could you please tell me:
What software or platform are you using (e.g., Excel, Google Sheets, Python/Matplotlib)?
What specific type of graph is creating the inappropriate result?
I can then give you tips to fix it or show you how to spot the distortion.
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