Scientific study has been around for thousands of years, but they way researchers have achieved a result from their hypothesis, has changed throughout time. Even within the last one hundred years confidence in scientific results have changed with the evolution of statistical analysis. Statistical analysis allows researchers to test their data for variables that may be present in order to gain a confidence level in their results. We see an example of the evolution of study from an article written by Arthur Tansley (1917) who studied competition between different plants in varying soils. Tansley published his paper using mean as a way to perform statistical analysis on his data, which only gave an average number for his dataset. The average number does not tell us much when it comes to data until it is further processed through a statistical test like an ANOVA or a T-test. These tests create an understanding and a confidence level that the data retrieved from an experiment are, or are not outside of a normal data range to be found. Researchers now are all required to perform these kinds of analysis on their data in order to be published in a journal. This is how modern research is able to give us more concrete results from their experiments as they account for the possibility of random chance in nature. The evolution of statistical analysis is creating new standards for research and learning from the past of how research is done allows for researchers to better their practice.
Applied Plant Ecology Winter 2020 