Top 10 trickiest Data analyst interview questions with answers for freshers

As a data analyst, you'll be working with data sets and using statistical methods to extract insights and make informed business decisions. During job interviews, employers often ask tricky questions to assess your analytical and problem-solving skills. In this blog, we'll cover the top 10 trickiest data analyst questions for freshers and provide detailed answers with examples.

What is the difference between population and sample?

Answer: In statistics, population refers to the entire group of individuals or objects that you want to study. On the other hand, a sample is a subset of the population that you select to gather information about the population. For instance, if you want to study the income of all working professionals in a city, the population would be all the working professionals in that city. However, if you collect data from a random selection of 500 working professionals, that's your sample.

Example: Let's say you want to determine the average age of all the employees in your company. The population would be all the employees, but you may not have the resources to gather data from every single employee. In that case, you could take a random sample of 100 employees and calculate the average age from that sample.

What is correlation and causation, and what's the difference between them?

Answer: Correlation is a statistical relationship between two variables. If two variables are correlated, it means that changes in one variable are associated with changes in the other variable. However, correlation doesn't imply causation, which means that just because two variables are correlated doesn't mean that one variable causes the other.

Example: Let's say you observe a strong correlation between ice cream sales and crime rates in a city. Does this mean that ice cream causes crime? Of course not! The correlation could be due to a third variable, such as temperature. As the temperature increases, people tend to eat more ice cream, and also tend to spend more time outside, which could lead to an increase in crime.

What's the difference between mean, median, and mode?

Answer: Mean, median, and mode are three measures of central tendency used in statistics. The mean is the average value of a data set, calculated by summing all the values and dividing by the number of values. The median is the middle value in a data set, with an equal number of values above and below it. The mode is the most common value in a data set.

Example: Let's say you have the following data set: 3, 5, 7, 7, 10. The mean is (3 + 5 + 7 + 7 + 10) / 5 = 6.4. The median is 7. The mode is 7, as it appears twice in the data set, which is more than any other value.

What is a standard deviation?

Answer: Standard deviation is a measure of the spread or dispersion of a data set. It shows how much the values in a data set vary from the mean. A low standard deviation means that the values in a data set are close to the mean, while a high standard deviation means that the values are spread out over a wider range.

Example: Let's say you have two data sets, A and B. The mean of both data sets is 10. However, data set A has a standard deviation of 1, while data set B has a standard deviation of 5. This means that the values in data set A are much closer to the mean than the values in data set B, which are more spread out.

What's the difference between a histogram and a bar chart?

Answer: A histogram and a bar chart are two types of graphical representation used in data analysis. A histogram is used to represent the distribution of a continuous variable, while a bar chart is used to represent the distribution of a categorical variable.

Example: Let's say you want to visualize the distribution of heights of all the students in a class. You would use a histogram to plot the frequency of heights within certain ranges, such as 150-160 cm, 160-170 cm, and so on. On the other hand, if you want to visualize the distribution of the grades of the students in the class, you would use a bar chart to plot the frequency of each grade, such as A, B, C, and so on.

What is a p-value, and what does it signify?

Answer: A p-value is a measure of the probability of observing a certain result by chance, assuming that there is no significant difference between two groups or variables. In hypothesis testing, if the p-value is less than the significance level (usually 0.05), we reject the null hypothesis and conclude that there is a significant difference between the groups or variables.

Example: Let's say you want to test if there is a significant difference in the average salary between male and female employees in a company. You could conduct a hypothesis test by calculating the p-value. If the p-value is less than 0.05, you can conclude that there is a significant difference in salaries between the two groups.

What is regression analysis?

Answer: Regression analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It helps to identify the strength and direction of the relationship between the variables and make predictions based on the data.

Example: Let's say you want to predict the sales of a product based on its price. You could use regression analysis to model the relationship between the sales (dependent variable) and the price (independent variable). The regression model would provide you with the equation of the line of best fit, which you could use to make predictions about the sales at different price points.

What is a confidence interval?

Answer: A confidence interval is a range of values that is likely to contain the true value of a population parameter with a certain degree of confidence. It is calculated from a sample statistic and takes into account the variability of the sample.

Example: Let's say you want to estimate the average height of all the students in a school. You could take a random sample of 100 students and calculate the sample mean and standard deviation. Using these values, you could calculate a 95% confidence interval for the population mean, which would give you a range of values that is likely to contain the true population mean with 95% confidence.

What is data cleaning, and why is it important?

Answer: Data cleaning is the process of identifying and correcting errors and inconsistencies in data sets. It is important because inaccurate data can lead to incorrect conclusions and decisions.

Example: Let's say you are analyzing sales data for a company and you notice that some of the sales records are missing values or have incorrect values. You would need to clean the data by filling in the missing values or correcting the errors before analyzing the data to ensure that your conclusions are accurate.

As a Data analyst, how to deal with the missing data?

Handling missing data is a common problem in data analysis, and there are several approaches that can be used to address it. Here are some common strategies:

Deletion: One approach is to simply remove any rows or columns that contain missing data. This method can be useful if the missing data is small in proportion to the overall dataset, but it can also result in a loss of valuable information.

Imputation: Another approach is to estimate the missing values based on the values of other variables in the dataset. There are several methods for imputing missing data, including mean imputation, regression imputation, and multiple imputation. Imputation can help retain more of the data and improve the accuracy of analysis, but it also introduces uncertainty and assumptions.

Analysis based on available data: Some analysis can be conducted using only the available data, without imputing missing values. This can be useful if the missing data is only a small portion of the dataset and the analysis does not require complete data.

Incorporate the missing data as a separate category: In some cases, the missing data itself can be used as a separate category in the analysis. This can help retain information on the missing data while conducting analysis.

The choice of strategy will depend on the specifics of the dataset and the analysis being conducted. A good data analyst should carefully evaluate the available options and choose the approach that is most appropriate for their specific needs

What is the difference between a Type I error and a Type II error?

Answer: A Type I error occurs when we reject a null hypothesis that is actually true. A Type II error occurs when we fail to reject a null hypothesis that is actually false.

Example: Let's say you conduct a hypothesis test to determine if a new drug is effective in treating a certain disease. If you reject the null hypothesis (the drug is not effective) when it is actually true, this is a Type I error. This means that you would conclude that the drug is effective when it is not, leading to potentially harmful consequences for patients. On the other hand, if you fail to reject the null hypothesis (the drug is not effective) when it is actually false, this is a Type II error. This means that you would conclude that the drug is not effective when it actually is, leading to missed opportunities for treatment.

Conclusion:

Data analysis is an essential skill for any data analyst or scientist, and being able to answer tricky questions is a crucial part of the job. By understanding these top 10 trickiest data analyst questions and their answers, you can prepare yourself for interviews and excel in your career. Remember to practice your skills and stay up-to-date with the latest techniques and technologies in data analysis to stay ahead of the curve. Good luck!