Statistics play a significant role in the life of a data scientist. Extensive knowledge of statistics helps the data professional make better business decisions. 

Inferential statistics help infer properties of the population taken from any given data set and descriptive statistics aids in making us understand the data along with the properties with the help of central tendency and variability. 

As an aspiring data science specialist, the below statistics questions may come in handy while giving your first interview. Let’s delve deeper and learn the questions you’re likely to come across. 

• Define confidence interval?

Confidence interval is said to be the interval estimation of parameters that can be extracted via statistical inference. Therefore, it is calculated using the formula below, 

[point_estimation – cv*sd, point_estimation + cv*sd]

Wherein, 

cv – defined as the critical value according to the sample distribution

sd – standard deviation of the given sample

• Can you define confidence level?

The confidence level defined in the hypothesis testing is said to be the probability of rejecting a null hypothesis provided it is a false one. The formula to calculate this is,

P(Not Rejecting H0|H0 is True) = 1 – P(Rejecting H0|H0 is True)

Where the default statistical power is said to be at 95 percent.

• Please define hypothesis testing?

Hypothesis testing can be defined as a method of statistical inference out of which you calculate the probability (p-value) of observing the statistics from the given data and conclude only if the null hypothesis is true. Now based on this you would have to decide whether or not you need to reject the null hypothesis by comparing the p-value and the significance level. The testing is majorly used for testing the existence of an effect. 

• How can you detect outliers?

Detecting outliers is as simple as defining the difference. Outliers are nothing but observations that can differ differently from other observations and the easiest way you can plot the variable is by detecting the data points which are far from others. Now the only way to quantify such differences is by using quartiles or interquartile range (IQR). Interquartile Range can be detected when you minus the first quartile i.e. Q3-Q1. The outliers can be defined as any data point which is lesser than Q1–1.5*IQR or maybe higher than Q3+1.5*IQR. 

• How will you define p-value?

P-value is defined as the probability to observe data provided the null hypothesis is true. If the p-value is small, it means there’s a higher probability of rejecting the null hypothesis. 

• Can you define Type I and Type II error?

Type I error can be defined as P Rejecting H0|H0 is True) which is false positive (where ⍺ is defined as one minus the confidence level) and Type II error is defined to be P (Not Rejecting H0|H0 is False) (where β, is defined as one minus statistical power) and false negative. 

However, there can be a slight trade-off between both Type I and Type II errors. This simply means if you wish to decrease Type I error, you’ll probably have to increase Type II error. 

• Is there a way to choose a sample size for an experiment?

The sample size is said to closely relate with the sample’s standard error, the power, effect size, and the desired level of confidence. The sample size is said to increase only when the power increases or when the sample effect size is decreased. Statistics is a fundamental tool of a data science specialist, one of the major reasons why every professional from the data science domain needs to have in-depth knowledge in this field. 

• Define standard error?

The standard error is defined as the standard deviation of a sampling distribution. With the help of CLM, the standard error of the mean can be defined using the population standard deviation which is divided by the square root by taking the sample size n. Take for instance if the population standard is said to be unknown the standard deviation can be used as an estimation. 

Most often people hesitate to take up data science certificate programs because they feel it is not valuable in the industry. To be precise, adding a certification to your skill set will not only add more weightage to your resume but will also help you get offered with more job opportunities.

To set yourself apart from the crowd, you will need to take up data science certifications that will give you industry exposure and quality projects. Certifications are considered as a standard that measures great talent in the given field. 

Therefore, if your wish is to become a data science professional you will need to master statistics. Data science has become one of the glamorous roles over the years. However, many people apply for the said roles but they don’t have the right set of skills. Also, one of the major reasons why employers tend to prefer candidates having certifications. In a nutshell, certification is a great way to learn data science.