tt假定数据来自一个正常的说tribution and that a reasonable large sample size is used. Your sample data appear to violate both assumptions. Instead of using a parametric test, I highly urge you to use bootstrap confidence intervals that are free from these assumptions (but still requires a few samples, of course).
This demo uses
bootci()
from the Stats and Machine Learning toolbox to compute the 95% confidence interval of the sample based on the percentile method. It increases your sample size to 1000 by sampling with replacement to compute the 95% CI. If the expected mean (mu) is outside that interval, you can assume that the data come from a different distribution than the distrubtuion used to calculate the expected mean.
mu = 2.366;% Population mean
sample=[2.180213,2.178298 ,2.310851 ,2.114255 ,3.012553 ,2.69234 ,2.079787];
bci = bootci(1000, {@mean, sample},'Alpha', 0.05,'Type','per')
isdiff = ~all(sign(mu-bci)==[1;-1])% 0/false means mu is within the 95% CI of the sample
If you do not have the Stats and ML toolbox or if you'd like to see what's going on in
bootci
, see
this answer
that computes 90% CIs using
bootci
and by computing the bootstrapped means and CI directly without any toolboxes.
