It's a simple question that exposes a deep flaw in how traditional spreadsheets handle even basic data visualization. When a new user wants to apply a unique error bar to each point on a graph, the tool should make that intuitive, not force them to search forums for workarounds. The fact that Excel applies one custom error bar to every data point by default is a design choice that prioritizes the software's internal logic over the user's actual workflow.
For anyone working with experimental data, survey results, or any dataset where each measurement carries its own uncertainty, this limitation isn't just an inconvenience, it's a barrier. You have to manually create separate series, dig into obscure dialog boxes, or copy-paste values into specific ranges that the program can interpret. This process is error-prone and discourages newcomers from accurately representing their data. The frustration in that Reddit thread is not about a lack of effort; it's about a tool that assumes all your data points behave the same way, which is rarely true in real-world analysis.
What this tells us is that the spreadsheet model, as it stands, is still built on assumptions from an era before AI could handle complexity. The user shouldn't need to learn a hidden syntax or consult a community thread just to make each bar reflect its own margin of error. A modern, AI-native approach would recognize the pattern in your data and ask: "Would you like each point to use its own error value from this column?" It would handle the mapping automatically, freeing you to focus on what the error bars actually mean for your conclusions.
The practical lesson here is straightforward: if you find yourself fighting a spreadsheet to do something that should be simple, it's not you. The tool is outdated. The best response is to explore alternatives that treat your data as individual observations, not as a uniform block. Your time is better spent interpreting the variation in your data than wrestling with a dialog box that refuses to listen.