Question about SPM result... am I missing something?

When conducting multiple comparisons, such as in your SPM analysis, it is common to adjust the alpha level to account for the increased probability of obtaining false positives. One common adjustment method is the Bonferroni correction, which divides the desired alpha level by the number of comparisons being made. In your case, you mentioned setting the alpha value to 0.0083, presumably after applying the Bonferroni correction. However, it's important to note that statistical significance is not an all-or-nothing concept. Just because a p-value is slightly above the adjusted alpha level doesn't automatically make the result insignificant. The choice of alpha is somewhat arbitrary and depends on the specific study and field of research. In your example, you mentioned observing p-values of 0.010 and 0.014, which are slightly higher than your adjusted alpha of 0.0083. While these values do not meet the strict criterion for statistical significance based on your chosen alpha, they still suggest a relatively small probability of observing such results by chance alone. Therefore, it's worth considering these findings as potentially meaningful or deserving of further investigation.
Additionally, it's important to interpret statistical significance in the context of effect size, sample size, and the specific research question. A small effect size or a small sample size can make it more challenging to achieve statistical significance, even if the observed results are practically important. So, while statistical significance is typically determined using an alpha level, it's important to consider the specific context, effect size, and other factors when interpreting the results. Statistical significance should not be the sole criterion for determining the importance or relevance of findings in a study.
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Question about SPM result... am I missing something?