The significance of having this in your research is that the lowest price could be an outlier, which would skew data such as standard deviation or moving averages. You want to try and make your data as true as possible, and if the lowest data was an anomaly, it could misrepresent your data.
Standard deviation may be one that is affected because it will take into account that lowest data point. A way to avoid that is just omit it from your data set and you may get a more true set of numbers. However, you want to be sure that the lowest point is in fact an anomaly because if is not, you may be affected in your later research. You can flip this and take a look at highest value in the same light because you want to make sure your data is as true as possible.
This affects both fundamental and technical analysis because you can have a revenue number be severely out of line just the same way you may have a stock price over a certain period of time. The important part is just to figure out if the lowest value is a one time event or might it happen again.
Check out the Internet because there you will find how people use the lowest value in their research. They may omit it or they may include it, either way, find out if there is a drastic difference and apply it to your research. Test these different numbers on a demo account to first see if you find one is better over the other and then go from there. If you are really stuck, reach out to an investing and trading community and ask for their feed back. Bouncing ideas off of people who do this for a living is the best way to narrow you search and implementation of the data set.