1. For the list of observational study types below, [briefly!] describe each one (like why you might use it over another, what it's good for, etc).
   1. Simple Cross-Sectional: A cross-sectional study investigates a population at one specific moment. Scientists gather information from a varied group of people to determine the occurrence of a specific condition or trait. Cross-sectional studies help determine how common diseases, risk factors, or behaviors are in a population at a specific point in time. They offer a glimpse into the overall health of the population.

1. Retrospective Case-Control: In a case-control study, researchers compare people who have a particular outcome (cases) to those who do not have the outcome (controls). They examine the past to find possible risk factors. This method is helpful for studying uncommon diseases or outcomes and determining the factors linked to the condition.

1. Simple Longitudinal: Long-term studies track a specific group of people for a long time. Data is gathered at different times to see how things change over time. Useful for looking at growth, illness advancement, or treatment impact.
2. Prospective Case-Control: Like retrospective case-control studies, but data is collected in the future. Researchers find cases and controls and track them over time. Helpful for studying new conditions or evaluating risk factors before the outcome happens.
3. For the list of experimental designs below, [briefly!] describe each (like why you might use it over another, what it's good for, etc).
   1. Factorial: Factorial designs enable researchers to study the impacts of multiple independent variables at the same time. These variables can be adjusted at various levels. For instance, if you wish to examine the influence of both time of day and caffeine intake on cognitive performance, a 2x2 factorial design can be used to explore these effects.
   2. Cross-Over: Cross-over designs are frequently employed in clinical trials or when comparing treatments. Patients are given different treatments in a sequential manner. To assess the effectiveness of two pain medications, both are administered to the same group of patients, with a washout period in between.

1. Pre-Post: Pre-post designs analyze how an intervention affects a dependent variable by measuring it before and after the intervention. Assessing the effectiveness of a new teaching approach by comparing students' test results before and after its introduction.
2. Quasi-Experimental: Quasi-experimental designs are employed when it is not possible or ethical to randomly assign participants. These designs do not involve true randomization. For instance, studying the impact of a smoking cessation program in various communities without randomly assigning individuals.

1. Simple Laboratory Experiment: Researchers can manipulate variables and observe effects in a controlled laboratory setting during experiments. They are testing how a new drug affects blood pressure in this controlled environment.
2. Using the details from questions 1&2, make a decision tree to help you figure out what kind of design is going to be used. If you're not familiar with decision trees, the basic idea is to narrow down a bunch of options by asking a few simple questions. For example, we talked about this in the Exam 2 review and I gave an example of a decision tree to figure out what type of statistical test to use. I've given you a sample of the first level (question you would ask) of a potential decision tree below, but feel free to come up with your own decision points that make it easiest for you to differentiate the designs we discussed.

I need help on this question the decision tree