# Live technical interview by an AI

People & Blogs

## Introduction

In a recent simulated interview, Aaron, a computer science graduate from Brown University, showcased his knowledge and enthusiasm for software engineering and data science. Conducted by Alex, the interviewer, the session covered various topics, particularly focusing on Python programming and statistical hypothesis testing.

The conversation began with Aaron introducing himself, revealing his educational background and passion for the field. Alex confirmed that Aaron's ability to work in London was not an issue due to his dual citizenship in the United States and the UK.

The interview then moved on to a technical question about Python. Alex asked Aaron to explain the key differences between a tuple and a list. Aaron correctly identified that a tuple is immutable, while a list is mutable. When prompted to elaborate on these concepts, he explained that immutability means that the data cannot be changed once created, whereas mutability allows modification. Furthermore, Aaron provided a practical example of choosing a tuple over a list: tuples can serve as keys in dictionaries due to their immutable nature, while lists cannot.

The discussion shifted to a statistical hypothesis involving stock prices in relation to CEO resignations. Aaron articulated a clear approach to testing his hypothesis: collecting data on stock price changes following a CEO's resignation and utilizing statistical tests, such as the T-Test, to analyze the results. He emphasized the importance of specific data points, particularly the percent change in stock price 24 hours post-resignation.

When asked about setting up his null hypothesis, Aaron defined it as the assertion that the change in stock price is zero, contrasting it with the alternative hypothesis, which predicted a decrease in stock price. Alex praised his clear setup of the hypotheses and encouraged further discussion on ensuring the validity of the results. Aaron mentioned calculating the P-value and maintaining clean data, as well as cross-referencing from multiple data sources and utilizing regression analysis to account for other variables.

To wrap up the interview, Alex prompted Aaron for any questions he might have. Aaron inquired about salary expectations for the role, to which Alex responded with a range of $ 125,000 to $ 175,000, excluding bonuses and benefits.

The interview concluded on a positive note, with both parties expressing appreciation for the conversation.

## Keywords

- Python
- Tuple
- List
- Immutable
- Mutable
- Data science
- Hypothesis testing
- T-Test
- Statistical significance
- Null hypothesis
- Regression analysis
- Salary expectations

## FAQ

**Q: What is the primary difference between a tuple and a list in Python?**

A: The primary difference is that a tuple is immutable (cannot be changed), while a list is mutable (can be changed).

**Q: Why would you use a tuple as a key in a dictionary?**

A: Because tuples are immutable, they can serve as keys in dictionaries, whereas lists cannot.

**Q: How do you test a hypothesis related to stock prices?**

A: You would collect data on stock price changes after specific events (like a CEO resignation) and apply statistical tests such as a T-Test to analyze the data for significance.

**Q: What does the null hypothesis represent?**

A: The null hypothesis states that there is no effect or change—in this context, it asserts that the stock price change is zero.

**Q: What is an important step to ensure the validity of statistical results?**

A: Key steps include checking the P-value for significance, ensuring data cleanliness, and potentially using regression analysis to control for other variables.