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We explore the biasing in the clustering statistics of haloes as compared to dark matter (DM) in simulations. We look at the second‐ and third‐order statistics at large scales of the (intermediate) MICEL1536 simulation and also measure directly the local bias relation  h = f (δ) between DM fluctuations, δ, smoothed over a top‐hat radius  R  s  at a point in the simulation and its corresponding tracer  h  (i.e. haloes) at the same point. This local relation can be Taylor expanded to define a linear ( b  1 ) and non‐linear ( b  2 ) bias parameter. The values of  b  1  and  b  2  in the simulation vary with  R  s  approaching a constant value around  R  s  &gt; 30–60 Mpc  h  −1 . We use the local relation to predict the clustering of the tracer in terms of the one of DM. This prediction works very well (about per cent level) for the halo 2‐point correlation ξ( r  12 ) for  r  12  &gt; 15 Mpc  h  −1 , but only when we use the biasing values that we found at very large smoothing radii  R  s  &gt; 30–60 Mpc  h  −1 . We find no effect from stochastic or next‐to‐leading‐order terms in the  f (δ) expansion. However, we do find some discrepancies in the 3‐point function that needs further understanding. We also look at the clustering of the smoothed moments, the variance and skewness which are volume‐average correlations and therefore include clustering from smaller scales. In this case, we find that both next‐to‐leading‐order and discreteness corrections (to the local model) are needed at the 10–20 per cent level. Shot‐noise can be corrected with a term , where  σ 2  e  &lt; 1 , that is, always smaller than the Poisson correction. We also compare these results with the peak‐background split predictions from the measured halo mass function. We find 5–10 per cent systematic (and similar statistical) errors in the mass estimation when we use the halo model biasing predictions to calibrate the mass.

The local bias model in the large‐scale halo distribution