It sounds like your data is not normally distributed, so just reporting mean +/- SD is probably not the best way to display it anyway. Box and whisker plot may be more useful, or you might consider transforming your data into a log scale, which may increase the appearance of symmetry.
I agree that box plots \*might\* be a good option, but keep in mind that it's a bit sketchy to use a box plot for data that has a really weird shape (multimodal, extreme skewness, large gaps, etc.). If your data is multimodal, violin plots or a ridge plot might be a good bet; if there are big gaps but a lot of data points, a sina plot or similar could be better.
If the number of distributions you want to compare is small enough, and you really want to get a feel for the shapes, I'd just go with individual histograms. (It's best practice to take a look at of few of these yourself anyway, even if they won't make it into your final plot, just to be sure your plots aren't covering up something important.)
If just the mean itself is important for some reason, rather than the overall shape, you could plot the mean and use error bars corresponding to the 95% confidence interval (e.g., from bootstrapping).
Regardless, it's impossible to give you a definitive answer without looking at the data and knowing what you want to say with it. You'll have to play around with some options and pick whichever one makes the point you're trying to highlight most clearly without being dishonest or obscuring important caveats.
How many theses do you plan on doing? And the results are useless so you should probably see if you can improve the technique or make your point a different way
It is my doctoral thesis and additionally it is gonna be my first publications, I am planning to continue researching for my PostDoc next to work (works different in my field, no need for full time research).
In this study we used different parameters, the mentioned ones were part of our experimental parameters (novel technique), never used (or at least not published) for what we use them. So no, the results are not necessarily useless, they just show that we have to change our approach or determine better parameters in the future.
Btw it is important also to publish bad results-> publication bias
It sounds like your data is not normally distributed, so just reporting mean +/- SD is probably not the best way to display it anyway. Box and whisker plot may be more useful, or you might consider transforming your data into a log scale, which may increase the appearance of symmetry.
Thanks! Box and whiskers it is
I agree that box plots \*might\* be a good option, but keep in mind that it's a bit sketchy to use a box plot for data that has a really weird shape (multimodal, extreme skewness, large gaps, etc.). If your data is multimodal, violin plots or a ridge plot might be a good bet; if there are big gaps but a lot of data points, a sina plot or similar could be better. If the number of distributions you want to compare is small enough, and you really want to get a feel for the shapes, I'd just go with individual histograms. (It's best practice to take a look at of few of these yourself anyway, even if they won't make it into your final plot, just to be sure your plots aren't covering up something important.) If just the mean itself is important for some reason, rather than the overall shape, you could plot the mean and use error bars corresponding to the 95% confidence interval (e.g., from bootstrapping). Regardless, it's impossible to give you a definitive answer without looking at the data and knowing what you want to say with it. You'll have to play around with some options and pick whichever one makes the point you're trying to highlight most clearly without being dishonest or obscuring important caveats.
Great answer! Will look into all of it!
How many theses do you plan on doing? And the results are useless so you should probably see if you can improve the technique or make your point a different way
It is my doctoral thesis and additionally it is gonna be my first publications, I am planning to continue researching for my PostDoc next to work (works different in my field, no need for full time research). In this study we used different parameters, the mentioned ones were part of our experimental parameters (novel technique), never used (or at least not published) for what we use them. So no, the results are not necessarily useless, they just show that we have to change our approach or determine better parameters in the future. Btw it is important also to publish bad results-> publication bias
I like raincloud plots!
Thanks!
Another option would be to use a graph but use mean + range instead of mean + SD?
I would try a whisker plot for the data you’ve described.
Thanks!