Once upon a time, there was a young data scientist named Lily. She loved studying data and making predictions about the world around her. One day, she learned about histograms. Histograms are like bar charts that show how many times a certain value appears in a set of data.

Lily was fascinated by histograms and decided to use them to test her hypothesis. A hypothesis is an idea or prediction about something that can be tested. She wanted to know if more people would go to the park on sunny days or on cloudy days.

She collected data every day for a week, and then made a histogram to show the results. But just making the histogram wasn't enough. She also had to choose the right hyperparameters for her analysis. Hyperparameters are settings that can be adjusted to make the results better.

After carefully choosing the right hyperparameters, Lily was ready to make an inference. An inference is when you use information from your data to make a conclusion about something. And her inference was that more people went to the park on sunny days!

Lily was so proud of her work and went to show her friends. They were amazed by the histogram and her clever inference. From that day on, Lily used histograms and her data science skills to explore and learn about the world around her. The end.

Reflections

1. What is a histogram and how did Lily use it?
2. What is a hypothesis and what was Lily's hypothesis about the park attendance?
3. What is an inference and what was Lily's inference about the park attendance?
4. What is a hyperparameter and why was it important for Lily's analysis?
5. Why was Lily proud of her work and what did she do with her results?