Statistics Interview Questions – Day 12

Topic – Statistics Interview Questions

Statistics, in simple terms, is a way of collecting, organizing, analyzing, interpreting, and presenting data to understand various patterns and trends in the world around us. It helps us make sense of information and draw meaningful conclusions from it.

Here’s a basic example to illustrate the concept:

Imagine you’re curious about the average height of students in your school. You decide to measure the height of 20 randomly selected students. After collecting the data, you find that the heights range from 5 feet to 6 feet. To understand this information better, you calculate the average height of the 20 students, which turns out to be 5.5 feet.

Now, armed with this statistical insight, you can make certain inferences, such as “the average height of students in my school is around 5.5 feet.” This conclusion helps you understand the general trend without having to measure every single student’s height. Statistics essentially enables us to draw conclusions about a large group based on a smaller, manageable sample.

In a broader context, statistics can be used in various fields like sports to analyze player performance, in economics to understand market trends, in medicine to study the effectiveness of a treatment, and in many other areas to make informed decisions based on data.

Statistics Interview Questions

15 Most Asked Topics

• Simpson’s Paradox:
  • Definition: Simpson’s paradox occurs when a trend appears in different groups of data but disappears or reverses when these groups are combined. It highlights the importance of understanding the effects of lurking variables.
• Type I and Type II Errors:
  • Type I Error: It occurs when the null hypothesis is true but rejected. It’s the false rejection of a true null hypothesis.
  • Type II Error: It occurs when the null hypothesis is false but accepted. It’s the failure to reject a false null hypothesis.
• Central Limit Theorem (CLT):
  • Definition: The Central Limit Theorem states that the distribution of sample means approximates a normal distribution, regardless of the original distribution’s shape. It is crucial for making inferences about a population from a sample.
• Population vs. Sample:
  • Population: It refers to the entire group that you want to draw conclusions about.
  • Sample: It is a subset of the population that you use to make inferences about the entire population.
• P-value:
  • Definition: The p-value is a measure used in hypothesis testing to determine the strength of evidence against the null hypothesis. It helps in deciding whether to reject the null hypothesis or not.
• T-test and Z-test:
  • T-test: It is used to determine if there is a significant difference between the means of two groups.
  • Z-test: It is used when the sample size is large, and the population variance is known.
• Skewness and Kurtosis:
  • Skewness: It measures the asymmetry of a distribution.
  • Kurtosis: It measures the tailedness or sharpness of a distribution.
• Correlation vs. Causation:
  • Correlation: It refers to a relationship between two variables, but it does not imply causation.
  • Causation: It implies that one event is the result of the occurrence of another event.
• Standard Deviation and Variance:
  • Standard Deviation: It is a measure of the amount of variation or dispersion in a set of values.
  • Variance: It is the average of the squared differences from the mean.
• Parametric vs. Non-parametric tests:
  • Parametric tests: They make assumptions about the parameters of the population distribution.
  • Non-parametric tests: They do not make any assumptions about the parameters of the population distribution.
• Null Hypothesis and Alternative Hypothesis:
  • Null Hypothesis (H0): It is a statement of no effect or no difference, often the hypothesis to be tested or supported.
  • Alternative Hypothesis (H1): It is the opposite of the null hypothesis and represents the possibility of an effect or difference.
• Confidence Interval:
  • Definition: A confidence interval is a range of values that is likely to contain the population parameter with a certain degree of confidence. It provides a measure of the uncertainty associated with an estimate.
• Multicollinearity:
  • Definition: Multicollinearity occurs when two or more independent variables in a regression model are highly correlated. It can affect the accuracy and reliability of the regression results.
• Statistical Power and Effect Size:
  • Statistical Power: It is the probability of correctly rejecting a false null hypothesis. It is the ability of a test to detect an effect, given that the effect actually exists.
  • Effect Size: It is a quantitative measure of the strength of a phenomenon, typically used to determine the practical significance of a study’s results.
• One-tailed and Two-tailed Tests:
  • One-tailed Test: It is used to test whether the sample mean is significantly greater than or less than a known population mean.
  • Two-tailed Test: It is used to test whether the sample mean is significantly different from a known population mean

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