Hypothesis testing is a way to use data from a small group to make guesses about a larger group. It starts with an assumption, then gathers data, and finally it checks if the assumption holds up. In hypothesis testing, you look at two opposite ideas about a group which is the null hypothesis. This is the belief or is also a starting point. It's an idea that hasn't been proven yet but is assumed true for now. The alternative hypothesis is the idea that opposes the null hypothesis. It's what you think might actually be true instead. You use hypothesis testing when you want to decide something about a whole group based on a small sample. Researchers often use it to check if their ideas or models work. The One-Tailed vs Two-Tailed Tests checks if the data is either higher or lower than a certain value, but not both. If the data falls in this area, you accept the alternative idea instead of the null. A two-tailed test checks if the data is either much higher or much lower than a certain range. If it falls in either area, you accept the alternative idea instead of the null. An example of when I would use a hypothesis test would be at my current employment. An example would be setting my normal hypothesis to test how often to collect litter from the local parks. I need to gauge how much trash is collected on a monthly basis versus a weekly basis.. is it more impactful to collect monthly biweekly weekly or daily. If on a monthly basis, there is three times the amount of trash collected then a biweekly basis then we can judge how much litter is collected within that period time