First,
small errors tend to multiply as the number of iterations increases.
Not exact matches
«Every
small human
error — even the little
errors we all
tend to make — become reminders of their shortcomings.»
If it is broken down into groups that are too
small (e.g., individual classes) the standard
error of measurement
tends to become so great that although the data remains «valid» it is no longer «reliable.»
Tracking
errors tend to be
small, but they can still adversely affect your returns.
Because most AOGCMs have coarse resolution and large - scale systematic
errors, and extreme events
tend to be short lived and have
smaller spatial scales, it is somewhat surprising how well the models simulate the statistics of extreme events in the current climate, including the trends during the 20th century (see Chapter 9 for more detail).
Finally, although adjustments were made to correct for potential type I
error, it is important to note that these are correlational analyses, and effect sizes
tended to be
small to medium in size.