anchor.diff:      %%
       %% Verify that the basic statistics are ok
       %% be gentle we don't want to see to many failing tests
  -    abs(StandardMean) < 0.005 orelse ct:fail({average, Alg, StandardMean}),
  -    abs(StandardStdDev - 1.0) < 0.005 orelse ct:fail({stddev, Alg, StandardStdDev}),
  -    ok.
  +    if
  +        abs(StandardMean) < 0.005 -> [];
  +        true ->
  +            [{average, Alg, StandardMean, IntendedMean, IntendedVariance}]
  +    end ++
  +        if
  +            abs(StandardStdDev - 1.0) < 0.005 -> [];
  +            true ->
  +                [{stddev, Alg, StandardStdDev, IntendedMean, IntendedVariance}]
  +        end.
   
   normal_s(Mean, Variance, State0) when Mean == 0, Variance == 1 ->
       % Make sure we're also testing the standard normal interface
  
positive.code:         [{Mean, Variance} || Mean <- IntendedMeans,
                               Variance <- IntendedVariances],
  
      ct:timetrap({minutes, 6 * length(IntendedMeanVariancePairs)}), %% valgrind needs a lot of time
      lists:foreach(
        fun ({IntendedMean, IntendedVariance}) ->
                ct:pal(
                  "Testing normal(~.2f, ~.2f)~n",
                  [float(IntendedMean), float(IntendedVariance)]),
                [basic_normal_1(?LOOP, IntendedMean, IntendedVariance,
                                rand:seed_s(Alg), 0, 0)
                 || Alg <- [default|algs()]]
        end,
        IntendedMeanVariancePairs).
  
  
  basic_uniform_1(N, S0, Sum, A0) when N > 0 ->
      {X,S} =
          case N band 1 of
              0 ->
  
negative.code:             {Sum,A}
      end.
  
  basic_verify(Alg, Loop, Sum, AverExp, Buckets, Counters) ->
      AverDiff = AverExp * 0.01,
      Aver = Sum / Loop,
      io:format(
  