How to compare means, variances and standard deviations of durations for statistical significance I am trying to compare multiple mean values, variance and standard deviation values
for statistical significance. For example I have the following data:
Data 1


*

*Mean:            0.01304

*Sample Variance: 0.000504324897959184

*Standard Deviation: 0.0224571792075315


Data 2


*

*Mean:            1.17498

*Sample Variance: 0.180901244489796

*Standard Deviation: 0.425324869352588


How can I compare them?
 A: Because your data are durations, you should use methods from survival analysis.  A $t$-test is unlikely to be appropriate.  I doubt this can be done in Excel.  It isn't hard to do in R, however, and R is free.  You should download R from here.  This guide should be simple and quick enough to give you what you will need.  
What you want is to use a log rank test.  In R that's ?survdiff.  You may also want to plot and examine the Kaplan-Meier survival curves.  In R, you can use ?survfit and then plot().  Here's a quick demonstration from the R documentation:  
# install.packages(survival)  # if necessary
library(survival)

leukemia.surv <- survfit(Surv(time, status) ~ x, data = aml) 
windows()
  plot(leukemia.surv, lty = 2:3) 
  legend(100, .9, c("Maintenance", "No Maintenance"), lty = 2:3) 
  title("Kaplan-Meier Curves\nfor AML Maintenance Study") 


survdiff(Surv(time, status) ~ x, data = aml)
# Call:
#   survdiff(formula = Surv(time, status) ~ x, data = aml)
# 
#                  N Observed Expected (O-E)^2/E (O-E)^2/V
# x=Maintained    11        7    10.69      1.27       3.4
# x=Nonmaintained 12       11     7.31      1.86       3.4
# 
# Chisq= 3.4  on 1 degrees of freedom, p= 0.0653 

