Standard deviation vs. standard error for error bars in observation studies? I need some help understanding why error bars/confidence intervals (C.I.), i.e. plus/minus 1.96*standard error, are important in observation type studies. I understand them as: "If an experiment were repeated many times under the same conditions, 95% of those experiments would contain a correct parameter estimate, like a mean, and 5% of them would not. The part I find creepy is, we as the researcher, have no way of knowing whether our C.I. is in the 5% or not, especially if we don't repeat an experiment under the same conditions like with sampling in the natural environment. So, what's the point?
For something like sampling for fish, it's impossible to repeat a survey under the exact same conditions every time (time of day, location, sunlight, temp, air pressure, food source, currents, etc). Does the lack of control over factors like these (as opposed to a lab study), mean that standard deviation should be used instead (because it just describes the data we have without making assumptions about data we only collect hypothetically)?

When you report standard deviation (SD) you're describing how much your measurements vary between observations. More observations will give you a better estimate of the SD, but the estimate is not strictly dependent on sample size (see however https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation regarding the issue of bias in SD estimates).
When you report standard error, you are making a statement about how precisely you are able to estimate the population mean. The precision of this estimate is highly dependent on sample size: you get more precision in your estimates the more data you have.
Both descriptions of your data are valid, they just tell you different things. Standard deviation is a descriptive statistic, and it's always good to describe your data well. Sometimes it's better to simply display the data points you've collected, especially when there are few of them, or to report a histogram, especially when the data are not normally distributed.
Standard error is more relevant to comparisons between means, and you'll find the standard error in equations used to compare means, like the basic t-test. If you want to compare the means between different classes between observations, standard errors help demonstrate how confident you are in the group means you report.
See also https://stats.stackexchange.com/questions/32318/difference-between-standard-error-and-standard-deviation
There is no particular reason that these parameters should be used differently in observational or experimental research: both types of data can use both statistics.
