Frequently Asked Questions on Probability for Data Scientist Interviews.
Question 1: What is the difference between probability and likelihood?
Answer:
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Probability refers to the measure of the likelihood that an event will occur. It is a value between 0 and 1, where 0 indicates impossibility, and 1 indicates certainty. For a discrete random variable $X$, the probability of a specific outcome $x$ is denoted as $P(X = x)$.
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Likelihood is a concept used in statistical inference. It measures the support provided by the data for each possible value of the parameter. For a given parameter $ heta$ and observed data $x$, the likelihood is $L(\theta | x)$, which is often expressed as the probability of the observed data given the parameter, $P(x | \theta)$.
Question 2: Explain Bayes' Theorem.
Answer: Bayes' Theorem is a fundamental concept in probability theory and statistics that describes the probability of an event based on prior knowledge of related conditions. It is expressed as:
$$P(A | B) = \frac{P(B | A) \cdot P(A)}{P(B)}$$
Where:
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$P(A | B)$ is the posterior probability of event $A$ given event $B$.
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$P(B | A)$ is the likelihood of event $B$ given event $A$.
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$P(A)$ is the prior probability of event $A$.
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$P(B)$ is the marginal probability of event $B$.
Question 3: What are independent and mutually exclusive events?
Answer:
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Independent Events: Two events are independent if the occurrence of one does not affect the probability of the other. Mathematically, $P(A \cap B) = P(A) \cdot P(B)$.
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Mutually Exclusive Events: Two events are mutually exclusive if they cannot occur simultaneously. For mutually exclusive events, $P(A \cap B) = 0$.
Question 4: Define conditional probability.
Answer: Conditional probability is the probability of an event occurring given that another event has already occurred. It is denoted as $P(A | B)$ and is calculated using the formula:
$$P(A | B) = \frac{P(A \cap B)}{P(B)}$$
provided that $P(B) > 0$.
Question 5: What is a random variable?
Answer: A random variable is a variable that takes on different values based on the outcomes of a random experiment. There are two types of random variables:
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Discrete Random Variable: Takes on a finite or countable number of possible outcomes. For example, the roll of a die.
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Continuous Random Variable: Takes on an infinite number of possible values within a given range. For example, the height of people.
Question 6: Explain the Central Limit Theorem.
Answer: The Central Limit Theorem (CLT) states that the distribution of the sum (or average) of a large number of independent, identically distributed (i.i.d.) random variables approaches a normal distribution, regardless of the original distribution of the variables. Formally, if $X_1, X_2, \ldots, X_n$ are i.i.d. random variables with mean $\mu$ and variance $\sigma^2$, then the standardized sum:
$$Z = \frac{\sum_{i=1}^{n} X_i - n\mu}{\sqrt{n}\sigma}$$
approaches a standard normal distribution as $n \to \infty$.
Question 7: What is the Law of Large Numbers?
Answer: The Law of Large Numbers (LLN) states that as the size of a sample increases, the sample mean will get closer to the expected value (mean) of the population from which the sample is drawn. Formally, if $X_1, X_2, \ldots, X_n$ are i.i.d. random variables with expected value $E(X_i) = \mu$, then:
$$\lim_{n \to \infty} \frac{1}{n} \sum_{i=1}^{n} X_i = \mu$$
Question 8: What is a probability distribution?
Answer: A probability distribution describes how the values of a random variable are distributed. It provides the probabilities of occurrence of different possible outcomes. There are two types of probability distributions:
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Discrete Probability Distribution: For discrete random variables, e.g., Binomial distribution.
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Continuous Probability Distribution: For continuous random variables, e.g., Normal distribution.
Question 9: Explain the difference between a probability density function (PDF) and a cumulative distribution function (CDF).
Answer:
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Probability Density Function (PDF): For a continuous random variable, the PDF describes the likelihood of the random variable taking on a specific value. The total area under the PDF curve is 1.
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Cumulative Distribution Function (CDF): The CDF represents the probability that a random variable will take a value less than or equal to a specific value. It is expressed as:
$$F(x) = P(X \leq x)$$
for a random variable $X$.
To Be Continued in Part 2...
In the next part, we will cover frequently asked questions on statistics, including concepts like hypothesis testing, confidence intervals, regression analysis, and more. Stay tuned!
Tags: Data Science Basics, Data Scientist Interview, Interview Preparation, Machine Learning Interview, Probability Interview Questions, Probability Theory, Statistical Concepts, Statistics Interview Questions
