Statistical analysis is a science of collecting, organizing, exploring, interpreting, and presenting data and uncovering patterns and trends in a research study (Valcheva, 2020). There are two main types of statistical analysis that are used for research studies, descriptive and inference. Descriptive statistics describes the basic features of information, shows or summarizes data in a rational way and is a quantitative study while inference statistics is a type of statistical analysis used to study the relationships between variables within a sample, and you can test a hypothesis, make conclusions, generalizations or predictions about a bigger population (Valcheva, 2020). There are six different types of statistical tests that are used for research experiments/studies: Standard t-test, ANOVA (single-factor), Paired t-test, Regression, ANOVA (two-factor) and Chi-Square. I decided to conduct a hypothetical study to see the effect of tending to a garden and planting four types of flowers in ground soil (control group) vs potting soil (experimental group). My hypothesis states that the flowers in the potting soil will have better results of taller growth. In this type of study, a standard t-test would be used to analyze and compare the data obtained. A standard t-test would be an important statistical analysis to use for this type of study to collect data because it will allow the experimenter to conduct comparisions between the two groups being tested in order to develop a mean.

Why would a standard t-test be used for the above experiment opposed to a paired t-test?

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