Trying to make 2 data sets the same length

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Matthew on 28 Feb 2023
Commented: William Rose on 28 Feb 2023
I have two datasets. One is a 1x102437 and the other is 1x41716. I am trying to make them the same length so that I can perform a paired t-test on the data. How do I make the 1x41716 the same length as the 1x102437? I have tries using interp1 but keep running into trouble with this.

Answers (3)

Voss on 28 Feb 2023
Edited: Voss on 28 Feb 2023
If you want to "expand" the shorter dataset to match the length of the longer one using interp1, here's one way:
dataset1 = rand(1,51); % random data
dataset2 = rand(1,21);
n1 = numel(dataset1);
n2 = numel(dataset2);
x1 = 1:n1;
x2 = linspace(1,n1,n2);
dataset2_interp = interp1(x2,dataset2,x1);
hold on
hold on
title('Dataset 2 Expanded')

William Rose
William Rose on 28 Feb 2023
You can do it, but that does not mean you should do it.
On what basis do you justify the pairing of the samples from the long vector with the interoplated samples form the short vector?
As for generating an equal number of samples (but I don;t recommend doing it unless you have a good justification):
Let's call the vectors y1 (long) and y2 (short). Illustrate with example vectors that are 1000 times shorter than yours:
y1=rand(1,102); y2=rand(1,42);
To interpolate y2 to be as long as y1, you need associated x values. Create a vector x2:
Create a query vector, xq:
102 102
y2int has the same length as y1. But this does not mean each value in y2 is paired with a certain element in y1.
William Rose
William Rose on 28 Feb 2023
@Matthew, you mentioned "The only problem with this [ttest2()] is that it assumes that the data sets come from two independent samples." The paired t test (ttest()) also assumes independence of the individual samples from one another, and it assumes or makes use of the built-in pairing of the data. That second assumption might be quesitoned when the raw data is not paired point-by point, as in this case. Both the paired (ttest) and unpaired (ttest2) tests assume the samples are normally distributed with equal variance. If you don;t want to make assumptions about normality and equal variance, use the Wilcoxon rank-sum test, also known as the Mann-Whitney U test, without interpolation, on the unequal-size samples.
If you want to interpolate, and you want to do a paired test, without the assumptions of normality and equal variance that are inherent in a t test, do the sign test, which is the paired equivalent of the rank-sum test:

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Image Analyst
Image Analyst on 28 Feb 2023
What about ttest2?
help ttest2
TTEST2 Two-sample t-test with pooled or unpooled variance estimate. H = TTEST2(X,Y) performs a t-test of the hypothesis that two independent samples, in the vectors X and Y, come from distributions with equal means, and returns the result of the test in H. H=0 indicates that the null hypothesis ("means are equal") cannot be rejected at the 5% significance level. H=1 indicates that the null hypothesis can be rejected at the 5% level. The data are assumed to come from normal distributions with unknown, but equal, variances. X and Y can have different lengths. This function performs an unpaired two-sample t-test. For a paired test, use the TTEST function. X and Y can also be matrices or N-D arrays. For matrices, TTEST2 performs separate t-tests along each column, and returns a vector of results. X and Y must have the same number of columns. For N-D arrays, TTEST2 works along the first non-singleton dimension. X and Y must have the same size along all the remaining dimensions. TTEST2 treats NaNs as missing values, and ignores them. [H,P] = TTEST2(...) returns the p-value, i.e., the probability of observing the given result, or one more extreme, by chance if the null hypothesis is true. Small values of P cast doubt on the validity of the null hypothesis. [H,P,CI] = TTEST2(...) returns a 100*(1-ALPHA)% confidence interval for the true difference of population means. [H,P,CI,STATS] = TTEST2(...) returns a structure with the following fields: 'tstat' -- the value of the test statistic 'df' -- the degrees of freedom of the test 'sd' -- the pooled estimate of the population standard deviation (for the equal variance case) or a vector containing the unpooled estimates of the population standard deviations (for the unequal variance case) [...] = TTEST2(X,Y,'PARAM1',val1,'PARAM2',val2,...) specifies one or more of the following name/value pairs: Parameter Value 'alpha' A value ALPHA between 0 and 1 specifying the significance level as (100*ALPHA)%. Default is 0.05 for 5% significance. 'dim' Dimension DIM to work along. For example, specifying 'dim' as 1 tests the column means. Default is the first non-singleton dimension. 'tail' A string specifying the alternative hypothesis: 'both' "means are not equal" (two-tailed test) 'right' "mean of X is greater than mean of Y" (right-tailed test) 'left' "mean of X is less than mean of Y" (left-tailed test) 'vartype' 'equal' to perform the default test assuming equal variances, or 'unequal', to perform the test assuming that the two samples come from normal distributions with unknown and unequal variances. This is known as the Behrens-Fisher problem. TTEST2 uses Satterthwaite's approximation for the effective degrees of freedom. See also TTEST, RANKSUM, VARTEST2, ANSARIBRADLEY. Documentation for ttest2 doc ttest2
set1 = rand(1, 100);
set2 = rand(1, 50); % Second set has a different number of observations.
[h,p] = ttest2(set1, set2)
h = 0
p = 0.8317
William Rose
William Rose on 28 Feb 2023
@Image Analyst is right (as always, it seems to me) that ttest2 is a good option. Which is why I used it in my example above. It is more conservative than a paired t test, in the sense that it does not make any assumptions about paired-ness.

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