# What is difference in sampling distribution and sampling from distribution?

Do sampling distribution and sampling from distribution mean the same thing? I am interested in x~N($$\mu$$, $$\sigma$$).

Sampling from a distribution (verb) refers to the process of drawing a random value (or set of values) from a probability distribution. Your example $$X \sim \mathcal{N}(\mu, \sigma)$$ means that random variable $$X$$ is normally distributed (with the specified parameters). Generating a realization (i.e. particular value) of $$X$$ would be sampling from this normal distribution.
A sampling distribution (noun) is the probability distribution of a statistic computed from a random sample. That is, suppose we have a dataset containing $$n$$ points. The data were obtained by randomly sampling from some population or underlying probability distribution. The statistic is a quantity computed from the data (e.g. the mean). Imagine performing the same experiment (infinitely) many times: sample a new dataset and compute the statistic. The distribution of the statistic across these repetitions is the sampling distribution.