This is a statistical project that is part of the Udacity Machine Learning Nanodegree. Use descriptive statistics and a statistical test to analyze the Stroop effect, a classic result of experimental psychology. Give your readers a good intuition for the data and use statistical inference to draw a conclusion based on the results
In this project, you will investigate a classic phenomenon from experimental psychology called the Stroop Effect. You will learn a little bit about the experiment, create a hypothesis regarding the outcome of the task, then go through the task yourself. You will then look at some data collected from others who have performed the same task and will compute some statistics describing the results. Finally, you will interpret your results in terms of your hypotheses.
Statistics is a major component of data analysis, it allows you to investigate data and make inferences based on your observations. A foundation in statistics also allows you to be a consumer of analyses that others perform, and allows you to relate to the conclusions others have drawn from their investigations.
- This project will review the basic concepts of statistics, including:
- How to identify components of an experiment
- How to use descriptive statistics to describe qualities of a sample
- How to set up a hypothesis test, make inferences from a sample, and draw conclusions based on the results
This project is focused on statistics, including designing an experiment and testing a hypothesis. Using statistics to draw valid conclusions about data is an important part of a data analyst's work. When you see a company release a new feature, what do you think went into the decisionmaking? Typically, a business has a goal they want to achieve - for Udacity, it may mean increased enrollments. We'd spend a long time user testing and A/B testing to make sure trials of potential products indicate we'd get the outcome we wanted.