  We describe how to implement the spectral kurtosis method of interference
removal (zapping) on a digitized signal of averaged power values. Spectral
kurtosis is a hypothesis test, analogous to the t-test, with a null hypothesis
that the amplitudes from which power is formed belong to a `good' distribution
-- typically Gaussian with zero mean -- where power values are zapped if the
hypothesis is rejected at a specified confidence level. We derive
signal-to-noise ratios (SNRs) as a function of amount of zapping for folded
radio pulsar observations consisting of a sum of signals from multiple
telescopes in independent radio-frequency interference (RFI) environments,
comparing four methods to compensate for lost data with coherent (tied-array)
and incoherent summation. For coherently summed amplitudes, scaling amplitudes
from non-zapped telescopes achieves a higher SNR than replacing zapped
amplitudes with artificial noise. For incoherently summed power values, the
highest SNR is given by scaling power from non-zapped telescopes to maintain a
constant mean. We use spectral kurtosis to clean a tied-array radio pulsar
observation by the Large European Array for Pulsars (LEAP): the signal from one
telescope is zapped with time and frequency resolutions of 6.25 $\mu$s and 0.16
MHz, removing interference along with 0.27 per cent of `good' data, giving an
uncertainty of 0.25 $\mu$s in pulse time of arrival (TOA) for PSR J1022+1001.
We use a single-telescope observation to demonstrate recovery of the pulse
profile shape, with 0.6 per cent of data zapped and a reduction from 1.22 to
0.70 $\mu$s in TOA uncertainty.
