Statistical Significance - Problems and Solutions?

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There has been much recent discussion about problems associated with the use of 'statistical significance' and the resulting dichotomy; p<0.05 supports the presence of an effect, while p>0.05 supports no effect and the often very rigid interpretation. The p-value is a measure of 'strength of evidence' based on the data. Just because p>0.05 does not mean that there is 'no effect'; it simply means that the evidence to hand does not support an effect. A small sample size almost by definition, because of low power, will invariably mean that the result is 'non-significant', irrespective of whether in truth there is an effect of the targeted size.
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Here are two articles, one from the American Statistical Association and the other from Nature that explain the issues and offer some quite radical solutions.
https://www.tandfonline.com/doi/full/10.1080/00031305.2016.1154108
https://www.nature.com/articles/d41586-019-00857-9