Unfortunately, the authors of the technical report devote great effort to the discussion and analysis of the data in separate, small groups for any kind of c decay trend within the group, and report changes which can only be explained as technique refinement, as if they were unequivocally in support of c decay.They do, in one place, however, consider the whole body of data collectively.

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The range of uncertainty was not reported for many of the earliest measurements, so some of the data points are plotted without error bars.

Most of the data points after 1850 do have error bars, but they are too small, in most cases, to be seen on the scale of the graph.

When I analyzed the entire data set of 163 points using the standard, weighted, linear least squares method, the decay of c was determined to be: decay of c = 0.0000140 ± 0.0000596 km/s/year.

This result says pretty plainly that there is no discernible decay trend in the data set presented by Norman and Setterfield.

As the experiment is repeated, with successive refinements of technique and method, the results gradually and asymptotically approach what we may accept with some confidence to be a reliable description of events (1969, p. Thus, any gradual and asymptotic approach of the results of experiments to measure c to its present-day value needs to be carefully and critically scrutinized to determine if the effect is due to real, physical changes in the structure of the universe which have altered c, or if it is merely the result of "refinements of technique and method" of measurement.

It is also well known that a given body of data can be inadvertently manipulated due to subjective bias in such a way as to yield unwarranted conclusions.

In particular, the knotty problem of starlight from distant galaxies seems to be solved in a straightforward, naturalistic manner.

Implications, however, no matter how favorable, cannot substitute for the clear empirical basis necessary to substantiate this hypothesis.

The best way to avoid this problem in the current context is to treat the entire data set as a whole.