# Inferential Statistics (Data Science & ML)

> Hypothesis testing, confidence intervals, p-value, t-test, chi-square, ANOVA, linear regression

- 24 interview questions
- Mid-Level
- [Interview Questions: Data Science & ML](https://sharpskill.dev/en/technologies/data-science/interview-questions.md)

## 1. What is the null hypothesis (H₀) in hypothesis testing?

**Answer**

The null hypothesis (H₀) is the default statement that assumes no effect or significant difference exists. It is the hypothesis we seek to reject or fail to reject based on the collected data. For example, H₀ might state that a new treatment has no effect compared to a placebo. The statistical test evaluates whether the data provides sufficient evidence to reject this hypothesis in favor of the alternative hypothesis (H₁).

## 2. What does the p-value represent in a statistical test?

**Answer**

The p-value is the probability of obtaining a result at least as extreme as the one observed, assuming the null hypothesis is true. A low p-value (typically < 0.05) indicates that the observed data is unlikely under H₀, leading to its rejection. Note: the p-value is not the probability that H₀ is true, nor is it the probability that results are due to chance.

## 3. What is a Type I error in inferential statistics?

**Answer**

A Type I error (false positive) occurs when we reject the null hypothesis when it is actually true. The significance level α (often 0.05) represents the maximum acceptable probability of making this error. For example, concluding that a drug is effective when it is not constitutes a Type I error. This error is controlled by the choice of significance level.

## 21 more questions available

- What is a Type II error in inferential statistics?
- What does a 95% confidence interval represent?

Sign up for free: https://sharpskill.dev/en/login

## Other Data Science & ML interview topics

- [Python Basics](https://sharpskill.dev/en/technologies/data-science/interview-questions/python-basics.md): 25 questions, Junior
- [Python Object-Oriented Programming](https://sharpskill.dev/en/technologies/data-science/interview-questions/python-oop.md): 20 questions, Junior
- [Python Data Structures](https://sharpskill.dev/en/technologies/data-science/interview-questions/python-data-structures.md): 20 questions, Junior
- [Git Fundamentals](https://sharpskill.dev/en/technologies/data-science/interview-questions/git-fundamentals.md): 18 questions, Junior
- [SQL Basics](https://sharpskill.dev/en/technologies/data-science/interview-questions/sql-basics.md): 20 questions, Junior
- [NumPy Fundamentals](https://sharpskill.dev/en/technologies/data-science/interview-questions/numpy-fundamentals.md): 22 questions, Junior
- [Pandas Basics](https://sharpskill.dev/en/technologies/data-science/interview-questions/pandas-basics.md): 22 questions, Junior
- [Jupyter & Google Colab](https://sharpskill.dev/en/technologies/data-science/interview-questions/jupyter-colab.md): 16 questions, Junior
- [SQL Joins & Advanced Queries](https://sharpskill.dev/en/technologies/data-science/interview-questions/sql-joins-advanced.md): 22 questions, Mid-Level
- [Advanced Pandas](https://sharpskill.dev/en/technologies/data-science/interview-questions/pandas-advanced.md): 24 questions, Mid-Level
- [Visualization with Matplotlib & Seaborn](https://sharpskill.dev/en/technologies/data-science/interview-questions/matplotlib-seaborn.md): 20 questions, Mid-Level
- [Interactive Visualizations with Plotly](https://sharpskill.dev/en/technologies/data-science/interview-questions/plotly-interactive.md): 18 questions, Mid-Level
- [Descriptive Statistics](https://sharpskill.dev/en/technologies/data-science/interview-questions/statistics-descriptive.md): 20 questions, Mid-Level
- [Web Scraping](https://sharpskill.dev/en/technologies/data-science/interview-questions/web-scraping.md): 18 questions, Mid-Level
- [BigQuery & Cloud Data](https://sharpskill.dev/en/technologies/data-science/interview-questions/bigquery-cloud.md): 18 questions, Mid-Level
- [Feature Engineering](https://sharpskill.dev/en/technologies/data-science/interview-questions/feature-engineering.md): 22 questions, Mid-Level
- [Supervised ML: Regression](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-supervised-regression.md): 24 questions, Mid-Level
- [Supervised ML: Classification](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-supervised-classification.md): 24 questions, Mid-Level
- [Decision Trees & Ensembles](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-trees-ensembles.md): 24 questions, Mid-Level
- [Unsupervised ML](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-unsupervised.md): 22 questions, Mid-Level
- [ML Pipelines & Validation](https://sharpskill.dev/en/technologies/data-science/interview-questions/ml-pipelines-validation.md): 22 questions, Mid-Level
- [Time Series & Forecasting](https://sharpskill.dev/en/technologies/data-science/interview-questions/time-series-forecasting.md): 22 questions, Mid-Level
- [Deep Learning Fundamentals](https://sharpskill.dev/en/technologies/data-science/interview-questions/deep-learning-fundamentals.md): 24 questions, Senior
- [TensorFlow & Keras](https://sharpskill.dev/en/technologies/data-science/interview-questions/tensorflow-keras.md): 22 questions, Senior
- [CNN & Image Classification](https://sharpskill.dev/en/technologies/data-science/interview-questions/cnn-image-classification.md): 24 questions, Senior
- [RNN & Sequences](https://sharpskill.dev/en/technologies/data-science/interview-questions/rnn-sequences.md): 22 questions, Senior
- [Transformers & Attention](https://sharpskill.dev/en/technologies/data-science/interview-questions/transformers-attention.md): 24 questions, Senior
- [NLP & Hugging Face](https://sharpskill.dev/en/technologies/data-science/interview-questions/nlp-huggingface.md): 24 questions, Senior
- [GenAI & LangChain](https://sharpskill.dev/en/technologies/data-science/interview-questions/genai-langchain.md): 24 questions, Senior
- [MLOps & Deployment](https://sharpskill.dev/en/technologies/data-science/interview-questions/mlops-deployment.md): 24 questions, Senior

---

Source: SharpSkill (https://sharpskill.dev), tech interview preparation for your real stack.
HTML version of this page: https://sharpskill.dev/en/technologies/data-science/interview-questions/statistics-inferential