Standard error = s / √n

where:

• s: sample standard deviation
• n: sample size

https://www.statology.org/standard-error-of-mean-google-sheets/

If a mean is +/- 0.06. it's not a mean

, Sample means are unique. best wishes, David Booth

( ± 0.06) could indicate different things. Be sure the author is indicating that this is the standard error of the mean.

Say θ∈RD and ℓ(θ) is the log-likelihood. Then ∇2ℓ(θ) is the Hessian of the log-likelihood (the DxD matrix of the partiel second derivatives for θ). The observed Fisher information is I* = −∇2ℓ(θ∗) with θ∗ being the vector of the maximum likelihood estimates. The standard errors are the square-roots of the diagonal of the inverse Fisher Information:

se = sqrt( diag(I∗−1) )

Example: exponential distribution with parameter θ

L(x) = θ·exp(-θ·x)

ℓ(x) = log(θ) - θ·x

ℓ(x) = n·log(θ) - θ·Σixi (for a sample of n values x = (x1, x2, ..., xn))

∇1ℓ(θ) = n/θ - Σixi

∇2ℓ(θ) = -n/θ²

θ*: ∇1ℓ(θ*) = 0 ↔ n/θ* - Σixi= 0 ↔ n/θ* = Σixi·Σ ↔ n/Σixi·Σ =θ* = 1/x̅

I* = -∇2ℓ(θ*) = n/(θ*)²

se = sqrt( (n/(θ*)²)−1 ) = sqrt(1/n) · θ*

I am grateful for your help, thanks a lot Jochen Wilhelm Sal Mangiafico David Eugene Booth Anton Vrdoljak