The p-value represents the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming the null hypothesis is true. A confidence interval provides a range within which the population mean is likely to fall. The variance measures how far data points are spread out from the mean and is calculated as the sum of squared deviations from the mean divided by the sample size minus one. The coefficient of variation allows for more meaningful comparison of distributions with different magnitudes than just comparing standard deviations. The standard error of the sample mean represents the variability of the sample mean as an estimate of the true population mean.

1. The p - value is simply the probability of getting a value as extreme or
more extreme than the actual value of the observed statistic when the
null hypothesis is true.
Confidence interval for a mean:The range with in which the population
mean is likely to lie
The variance: the sum of the squared deviation of the values from the
mean divided by sample size minus one.
Coefficient of Variation: When two distributions have means of
different magnitude, a comparison of the C.V. is therefore much more
meaningful than a comparison of their respective s.d.
Standard Error of the Sample mean ( S.E. ): The sample mean is
unlikely to be exactly equal to the population mean.
The standard error measures the variability
of the mean of the sample as an estimate of
the true value of the mean for the population
from which the sample was drown.