Not exact matches
The Bonferroni
correction divides the
type I (α)
error (0.05) by the number of comparisons in the analysis to yield a more conservative p value that is denoted to be statistically significant.
Whereas enthusiasm for fasting is increasing, clinical relevance remains low because of insufficient human data, including almost nonexistent controlled trials (21, 33 — 36), few clinical outcomes studies (37, 38), lack of
correction for inflated
type I
error rates from multiple hypothesis tests, and limited safety data (39 — 41).
Considering that the IPCC report is probably best viewed as a meta study and being generous with the assumption that all studies subsumed by it were also properly conducted at 95 %, there would still have to be some
correction for the
type 1
errors that are guaranteed to arise with this approach.
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Although we did not have a between - subjects factor, within - subjects follow - up contrast tests, with a Bonferroni
correction to minimize
type 1
error, were conducted to first establish the baseline period (i.e., no significant differences from the initial to pre-treatment assessments) followed by contrasts between the pre-treatment and post-treatment as well as follow - up assessments.
However, it is also important to recognize that
Type II
errors that exclude true differences due to overly strict
corrections are as important to avoid as
Type I
errors (Perneger, 1998).
The increase in
Type - I
error due to multiple statistical comparisons was controlled through the Simes
correction procedure [47], a corrective method which offers a more powerful test than the classic Bonferroni -
correction.