The real fix here is deceptively simple: make sure your positive and negative error bars point to the same data range. The user who posted this problem, spending who knows how long wrestling with oversized standard deviation bars, discovered that Excel, by default, sets the negative error bar direction to a value of one. That single default setting, invisible unless you dig into the formatting menu, was inflating their error bars beyond any reasonable interpretation of their data. The solution worked. But the fact that it took a Reddit post, screenshots, and multiple attempts to find it says something about how traditional spreadsheet tools treat their users.
What this means for you is practical. When you add standard deviation error bars in Excel, the software applies a generic setting that assumes a single-direction error bar unless you explicitly override it. The user's steps were correct: they selected the right column, chose Custom, and pointed to their StDev range. But the negative error bar direction remained at the default of one, while the positive direction used their custom range. That mismatch created the visual chaos. The fix, selecting the same range for both positive and negative, took seconds once they knew where to look. The lesson is not that the user made a mistake. It's that the tool buried a critical setting under layers of menus, with no warning that the default was wrong for their use case.
We think this is a symptom of a larger problem. Spreadsheet software was built decades ago for a world where data analysis meant manually calculating formulas and hoping for the best. Error bars were an afterthought, not a core feature. Users are expected to know the quirks, where defaults hide, which menus to open, what "Custom" actually means in context. That expectation is outdated. Modern tools should anticipate what a user intends, not require them to hunt through forums for a workaround. The user's own admission, "not too sure what any of that means, but it worked", captures the frustration perfectly. They solved the problem without understanding why it existed in the first place.
If you are building workflows around data visualization, this is the kind of friction that erodes trust. You cannot afford to wonder whether your error bars are correct or whether a hidden default is distorting your results. The fix exists, and it works. But the real opportunity is to move toward tools that eliminate these surprises, tools that align positive and negative error bars by default, that surface the logic behind every setting, and that treat your data with the respect it deserves. You should not have to guess.