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tokens: where each DLA is detected. The purity of the catalogue should increase with CNR, so any effect of impurities should decrease rapidly with the threshold we impose on CNR. We show this in Fig. 4 , where we see again an increased variation of DLA bias with metal strength as the minimum required CNR is increased. Figure 4. Bias of the DLAs against S (left-hand panel) and (right-hand panel) for different cuts in CNR. Red squares (CNR > 4) and blue triangles (CNR > 2) have been horizontally shifted to avoid overlap. Furthermore, in this case we see a larger variation of the bias for the smallest value of S : a higher threshold in CNR results in a lower bias factor for the DLAs with the smallest metal strength. The variation is highly significant: taking into account that the sub-samples with CNR > 4 contain 70 per cent of the same DLAs in the sub-samples with CNR > 2, the expected statistical fluctuation in the difference of bias factors is 0.4σ, where σ stands for the error in the largest sample, with CNR > 2 (indicated by the blue error bar in the figure). The expected fluctuation comes from the fact that the sub-sample with CNR > 2 can be considered as the combination of two independent samples (one with CNR > 4, and another one with 2 CNR ≤ 4) that are combined with weight to construct the final estimator. This implies that the typical fluctuation is δσ CNR > 2 , where . This variation is opposite to our expectation if impurities caused only by noise were present in the catalogue, in which case false DLAs would have metal strength and bias consistent with zero. Our interpretation of this result is that the contaminants that are included in the catalogue as the CNR threshold is lowered are mostly regions of broad Ly α absorption in the forest, which correspond to absorbers of lower column density than DLAs but with sufficiently broad velocity dispersions to create a broad absorption line that is consistent with a DLA in the most noisy spectra. These absorbers generally have weaker associated metal lines because of their lower column densities and higher ionization level (most of the lines used to measure the metal strength are of low-ionization species; see Arinyo-i-Prats et al. 2018 ), and they are more highly biased than the DLAs with the weakest metal lines because they are associated with collapsed regions, or regions in the process of collapsing, with high velocity dispersion. This can explain why the presence of contaminants increases the bias factor, and actually decreases the variation we measure of the bias factor with metal strength. These type of absorbers and their average metal lines are likely similar to the systems studied by Pieri et al. ( 2014 ); a detailed study of the bias factor of these strong Ly α absorbers is separately being done (Blomqvist et al. in preparation). 4.3 Correction for the effect of metal strength measurement errors The errors in the measurement of mix the samples , and , as each DLA is classified to one of