I guess you should think about what your variables actually are. The tests would take something like:

Condition | A | B | C |
Cond. 1   |0.5|0.3|0.4|
Cond. 2   |1.1|1.0|0.9|

where A, B, and C are the persons and Cond. x the different conditions.

Here, one step is not one replicate! You take the average step length for each person under condition 1,...,n.

In python, you can do a paired t-test using

from scipy import stats
stats.ttest_rel()

The function takes two arrays that contain the values for the two conditions. You will also have to check the assumptions of the tests you are using; there are quite some tutorials in which the process is described in detail.

I guess you should think about what your variables actually are. The tests would take something like:

Condition | A | B | C |
Cond. 1   |0.5|0.3|0.4|
Cond. 2   |1.1|1.0|0.9|

where A, B, and C are the persons and Cond. x the different conditions.

Here, one step is not one replicate! You take the average step length for each person under condition 1,...,n.

Why take the average? Because you want to know "a persons step size" under a given condition. Steps as such are pseudo replicates here: It is one and the same person and one and the same condition---you just take multiple measurements to get a better approximation of what "the step size" of person X is in that situation. Now that you took several measurements, you know the (average) step size of person X. Then you can compare this step size with the step size of person X in a different set up. You repeat the same with N persons and you'll have N replicates.

In python, you can do a paired t-test using

from scipy import stats
stats.ttest_rel()

The function takes two arrays that contain the values for the two conditions. Those arrays are both of length N (that is the number of test persons) and, obviously, their values should both be in the same order (person A is the first in both arrays, person B the second, and so on). You will also have to check the assumptions of the tests you are using; there is quite a number of tutorials in which the process is described in detail.

I guess you should think about what your variables actually are. The tests would take something like:

Condition | A | B | C |
Cond. 1   |0.5|0.3|0.4|
Cond. 2   |1.1|1.0|0.9|

where A, B, and C are the persons and Cond. x the different conditions.

Here, one step is not one replicate! You take the average step length for each person under condition 1,...,n.

I don't know what your data looks like, so I cannot tell you how to do this in code.

I guess you should think about what your variables actually are. The tests would take something like:

Condition | A | B | C |
Cond. 1   |0.5|0.3|0.4|
Cond. 2   |1.1|1.0|0.9|

where A, B, and C are the persons and Cond. x the different conditions.

Here, one step is not one replicate! You take the average step length for each person under condition 1,...,n.

In python, you can do a paired t-test using

from scipy import stats
stats.ttest_rel()

The function takes two arrays that contain the values for the two conditions. You will also have to check the assumptions of the tests you are using; there are quite some tutorials in which the process is described in detail.