Being able to analyze quantitative data statistically is important because being able to say whether or not something is statistically significant or not is a way to validate your numbers internationally, in a way. Statistics is used all over the globe, so Scientists use statistics to show whether or not their data is significant or not. Being able to prove whether or not your data is significant either proves or disproves your hypothesis/experiment. Also using statistics, you could find a new way of looking or finding something different to do in another experiment similar to the one that you were completing.
An example of using statistical analysis would be the spider lab done in BIOL 251. In this lab, we took quantitative data from four different sites and encountered how many different types of species of spiders were in each site. This allowed us to show if there is a significant statistical data set within the experimental data. In this lab, we used the Dominance and Shannon-Weiner index of species diversity to look for which site had the most species of spiders. Basically, we were looking for which site has the most biodiversity of spiders in a statistically significant way. The data LED us to believe that site one had the greatest biodiversity because The Simpsons index was 0.1, H’ was 2.3, and the endemic number was 6. The conclusion that site one had the greatest biodiversity was made after statistical analysis showed that the data for site one was statistically significant. Which can be seen in the tables within the datasheet.
Another experiment to that we applied statistics to be the roadkill presentation done in BIOL 251. For this experiment, we used the Shannon-Weiner index equation to show the statistical significance to how many of one species was found as roadkill. We use the data to create different type of graphs and figures to show how many of these species of one type of species was found and on which road they were found on. For many of the graphs and figures, we showed the T value and P value which shows whether or not something is statistically significant or not. For this presentation what that basically meant is the two or more data sets we are trying to compare is there a significant statistical comparison between the two or is it more of just a coincidence.
BIOL 251 Research Showcase Presentation 2019.pdf
In Biology 251 we did a population demography in Farmville VA by counting the number of gravestones found in some of the different graveyards here in Farmville. We used the data we collected to create a life table. We use summarizing key demographic values, including age Pacific mortality, survivorship curve, and expectation of further life. The data we compiled we used to investigate the demographic patterns and processes, like the difference in survival rates or life expectancy of gender from a certain time period. The data we collected started with pre-1901 and then clustered the ages into 10-year intervals in the total number from this should equal the total amount of tombstones data we collected. We use statistics to show whether or not the data we collected for the life table was statistically significant. We focused a lot on gender differences for mortality rates between the different tenure increments. We found that post-1950 males versus post-1950 females, pre-1901 males versus post-1950 males, and pre-1901 females versus post-1950s females were all statistically significant. We also constructed line graphs to show the different trends of the average lifespans of the six different groups.
The Dead Speak about population Density.pdf
Statistical data shows whether or not the data is significant or not. Statistics helps show the significance of data because you could be looking at the data and see a pattern or you know something seems larger than everything else and do you think it’s significant. But after using statistics to see whether or not the data you think is significant or not you might be surprised. Data that you think might not be significant could actually be statistically significant, while data that looks like it should be significant, but it turns out it’s not statistically significant. Since statistics is used worldwide being able to prove with numbers that your data is significant holds more value than actually using words in some cases. Having numbers and math to back up your claims of something being significant holds more value in the scientific community than just saying it is significant.