本人是个统计菜鸟，投了一篇论文中用了多元线性回归，两年的数据，每年数据中自变量有5个，因变量有8个，分别作了逐步进入回归分析，两年的数据共作了16次。审稿意见如下：
If I understand the statistical analysis correctly, then separate stepwise regression analyses were conducted for each behavioral outcome variable (active time, retirement-sunset interval, etc.) for each year. If 16 separate analyses were conducted, then if alpha = 0.05, then the chance of type I error is very high, and some of the observed "significant" relationships are likely to be spurious. This problem could be addressed by reducing the alpha level, but this is likely to result in an unacceptably low statistical power, given the small sample sizes.  Alternatively, a smaller set of outcome variables specifically related to the study hypothesis could be chosen (it is unclear how playing time, for example, is related to the energy maximization hypothesis), or a multivariate approach considering several outcome variables simultaneously could be employed.

哪位大神能否指点一下，我不明白他说的解决方案具体是啥。比如减少因变量，感觉即便减少了也不会对剩下的变量结果有影响啊