Returns an estimate of the statistical significance and, optionally, the t-values.
function ttest ( ave1 : numeric, var1 : numeric, s1 : numeric, ave2 : numeric, var2 : numeric, s2 : numeric, iflag  : logical, tval_opt  : logical ) return_val : float or double
Scalars or arrays of any dimension (they must be the same dimensionality as each other). They represent the means (averages) calculated from two samples (i.e. sample means).var1
Scalars or arrays of the same dimensionality as ave1 and ave2. They represent the variances calculated from two samples (i.e. sample variances).s1
Must be the same dimensionality as ave1, or else scalars. These contain the number of statistically independent observations. If the data within the two samples are significantly autocorrelated, then s1 and s2 should contain the equivalent sample sizes. If ave1, ave2, var1, and var2 are arrays and s1 and s2 are scalars then these numbers will be used by all dimensions.iflag
Set to False if the two original samples are assumed to have the same population variance. Set to True if the two original samples are assumed to have different population variances. The latter applies the Welsh degree-of-freedom modification (Welsh's t-test).tval_opt
Set to True if the Student t-values are to be returned in addition to the statistical probabilities. Set to False if only the probabilities are desired.
If tval_opt is False, then the return array will be the same dimensionality as ave1. Otherwise, the return array will be dimensioned 2 x dimsizes(ave1). The return type will be double if any of ave1, var1, ave2, or var2 are type double, and float otherwise.
This function uses the Student's t-test to test the null hypothesis that the sample means are from the same population (i.e. H0: ave1=ave2). Rejection of the null hypothesis (i.e. acceptance of the alternative hypothesis) indicates that the sample means are from two different populations.
An option is provided to allow for testing when the population variances are assumed to be equal or different. The value(s) returned by ttest represent estimates of the statistical significance. Commonly, values of 0.10 or less are used as critical levels of significance. As with any test, caution is advised when interpreting results when there are few samples. Note: the user should specify the critical significance level prior to the calculation.
The results are independent of array order. However, all the arrays must be conformant (i.e. have the same order).
The returned probability is two-tailed. The ttest uses the incomplete beta function (betainc) to calculate the probability. Example 2 at betainc illustrates how to get the one-tailed probability. There is also a link where you can get one and two-tailed probabilities via the WWW.
The returned information (t-values) can be used to create confidence bounds.
Yhi = Yavg + tval*Ystd/sqrt(N) ; Yavg & Ystd are the sample mean and std. dev. Ylo = Yavg - tval*Ystd/sqrt(N)
Assume X and Y are one-dimensional arrays (they need not be the same size). Assume each of the values within X and Y are independent and that X and Y have the same population variance. The following is from: http://www.di.fc.ul.pt/~jpn/r/bootstrap/resamplings.html#bootstrap-to-find-pearson-correlation-of-two-samples
X = (/27,20,21,26,27,31,24,21,20,19,23,24,28,19,24,29,18,20,17,31,20,25,28,21,27/) ; treated Y = (/21,22,15,12,21,16,19,15,22,24,19,23,13,22,20,24,18,20/) ; control siglvl = 0.05 aveX = avg (X) ; 23.6 ; dim_avg_n (X,0) aveY = avg (Y) ; 19.222 varX = variance (X) ; 17.083 ; dim_variance_n (X,0) varY = variance (Y) ; 13.477 sX = dimsizes (X) ; 25 sY = dimsizes (Y) ; 18 ; Following not used; FYI only diffXY = aveX - aveY ; 4.378 iflag = True ; population variance similar tval_opt= False ; p-value only prob = ttest(aveX,varX,sX, aveY,varY,sY, iflag, True) if (prob.lt.siglvl) then . . . ; difference is significant end if print(prob)
prob will be a scalar containing the significance. It will range between zero and one. If prob < siglvl, then the null hypothesis (means are from the same population) is rejected and the alternative hypothesis is accepted.
If tval_opt is set to True:
probt = ttest(aveX,varX,sX, aveY,varY,sY, iflag, True) print(probt)
then probt will be a 1D array of length two where probt(0) will contain the probability and probt(1) will contain the t-value.
Variable: prob Type: float Total Size: 8 bytes 2 values Number of Dimensions: 2 Dimensions and sizes:  x  Coordinates: (0,0) 0.0007473998 ; p-value (1,0) 3.658246 ; t-valueExample 2
Assume aveX, varX, sX, aveY, varY, sY are dimensioned nlat x mlon. Then:
alpha = 100.*(1. - ttest(aveX,varX,sX, aveY,varY,sY, iflag, False))
will yield alpha dimensioned nlat x mlon. A significance of 0.05 returned by ttest would yield 95% for alpha. This is often done for plotting.
If tval_opt is set to True:
alphat = 100.*(1. - ttest(aveX,varX,sX, aveY,varY,sY, iflag, True))
then alphat will be 2 x nlat x mlon where the probabilities will be at (0,nlat,mlon) and the t-values will be at (1,nlat,mlon).
Assume aveX, stdX, aveY, stdY are dimensioned 12 x nlat x mlon and represent climatologies and interannual variabilities (represented here as standard deviations) for each month. Further assume sX and sY contain the number of statistically independent values. (Generally, there is no significant year-to-year autocorrelation of monthly data [e.g. successive Januaries].) So here, xX and sY are scalars indicating the number of years used.
prob = ttest(aveX, stdX^2, sX, aveY, stdY^2, sY, iflag, False)
will yield prob dimensioned 12 x nlat x mlon. Note that the standard deviations were squared to produce variances as required by the ttest function.
probt = ttest(aveX, stdX^2, sX, aveY, stdY^2, sY, iflag, True)probt will dimensioned 2 x 12 x nlat x mlon where the probabilities will be at (0,12,nlat,mlon) and the t-values will be at (1,12,nlat,mlon).
Assume x and y are dimensioned time x lat x lon where "time", "lat", "lon" are dimension names.
- Use NCL's named dimensions to reorder in time.
- Calculate the temporal means and variances using the dim_avg and dim_variance functions.
- Specify a critical significance level to test the lag-one auto-correlation coefficient and determine the (temporal) number of equivalent sample sizes in each grid point using equiv_sample_size.
- Estimate a single global mean equivalent sample size using wgt_areaave (optional).
- Specify a critical significance level for the ttest and test if the means are different at each grid point.
dimXY = dimsizes(x) ntim = dimXY(0) nlat = dimXY(1) mlon = dimXY(2) (1) xtmp = x(lat|:,lon|:,time|:) ; reorder but do it only once [temporary] ttmp = y(lat|:,lon|:,time|:) (2) xAve = dim_avg (xtmp) ; calculate means at each grid point yAve = dim_avg (ytmp) xVar = dim_variance (xtmp) ; calculate variances yVar = dim_variance (ytmp) (3) sigr = 0.05 ; critical sig lvl for r xEqv = equiv_sample_size (xtmp, sigr,0) yEqv = equiv_sample_size (ytmp, sigr,0) (4) xN = wgt_areaave (xEqv, wgty, 1., 0) ; wgty could be gaussian weights yN = wgt_areaave (yEqv, wgty, 1., 0) (5) iflag= False ; population variance similar prob = ttest(xAve,xVar,xN, yAve,yVar,yN, iflag, False)