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False-positive results are common when testing numerous hypotheses, while false negatives are caused by poor statistical power.
When subgroup analyses of a positive clinical trial are unrevealing, such findings are commonly used to argue that the treatment's benefits apply to the entire study population; however, such analyses are often limited by poor statistical power.
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In particular, if the exposure was chronic, few controls were available with discordant exposures in different time periods, and, as well as the case crossover design, our resultant case time control analysis could have been hampered by a poor statistical power compared to a conventional study [ 32].
Our risk based analysis is more effective than the conventional "one variable at a time" subgroup analysis, which is plagued by false negative results from poor statistical power and by false positives from multiple comparisons.
In fact, the available literature is biased by small samples with different compositions, poor statistical power and a substantial lack of agreement about the definition of dropout.
This leads to poor statistical power and restrictions on the design of the experimental paradigm.
This individual analysis has inherent limits linked to the high intra- and inter-patient variability of activation, giving rise to poor statistical power and subjective interpretation [18, 19].
Unfortunately, those studies often suffer from poor statistical power [21].
Thus, prior specification of a frequency band of interest could lead to poor statistical power for detecting any real positive effects.
Consequently, trials may be of poor statistical power [ 23].
First are the small number of participants and poor statistical power.
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