I am learning the error bars to use them in my research. I am confused about the difference between the standard error and confidence interval. Which one is better to show the statistical significance?
Standard error of the estimate refers to one standard deviation of the distribution of the parameter of interest, that are you estimating. Confidence intervals are the quantiles of the distribution of the parameter of interest, that you are estimating, at least in a frequentist paradigm.
Consider this example in R:
random_normal <- rnorm(n = 10, mean = 10, sd = 2) m <- lm(random_normal ~ 1) summary(m) Call: lm(formula = random_normal ~ 1) Residuals: Min 1Q Median 3Q Max -2.3769 -1.1994 -0.8571 0.4477 4.0356 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 10.7324 0.7154 15 1.13e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.262 on 9 degrees of freedom confint(m) > confint(m) 2.5 % 97.5 % (Intercept) 9.114169 12.35064
Please note, that CI (here it's conventionally set up to 95%) here are wider that estimated mean 10.7324+-0.7154. But they can be the same, if we'll set confidence interval to 68% level (instead of 95%), we'll get virtually the same answer, as 10.7324+-0.7154:
> confint(m, level = 0.67) 16.5 % 83.5 % (Intercept) 9.995764 11.46905
Side note: regressing with syntax
random_normal ~ 1 is the same as estimating the mean. It's just for convenience to quickly obtain standard errors and CI in this particular case.
Reporting both won't hurt, to my opinion. But CI's, in general, are more versatile.