How do you assess the statistical significance of an insight?
We need to perform
hypothesis testing to determine statistical significance. Will take following
- First will define null hypothesis and alternate hypothesis
- We will calculate p- value
- Last, we would set the level of the significance (alpha) and if the p-value is less than the alpha, you would reject the null — in other words, the result is statistically significant.
What is the Central Limit Theorem and why is it important?
- Central limit
theorem is very important concept in stats. It states that no matter the
underlying distribution of the data set, the sampling distribution would be equal
to the mean of original distribution and variance would be n times smaller,
where n is the size of sample
- The central limit
theorem (CLT) states that the distribution of sample means approximates a
normal distribution as the sample size gets larger.
- Sample sizes equal
to or greater than 30 are considered sufficient for the CLT to hold.
- A key aspect of CLT
is that the average of the sample means and standard deviations will equal the
population mean and standard deviation.
Suppose that we are interested in estimating the average height among all people. Collecting data for every person in the world is impossible. While we can’t obtain a height measurement from everyone in the population, we can still sample some people. The question now becomes, what can we say about the average height of the entire population given a single sample. The Central Limit Theorem addresses this question exactly.”
What is sampling? How many sampling methods do you know?
Data sampling is a statistical analysis technique used to select, manipulate and analyse a subset of data points to identify patterns and trends in the larger data set. It enables data scientists and other data analysts to work with a small, manageable amount of data about a statistical population to build and run analytical models more quickly, while still producing accurate findings.
- Simple random sampling: Software is used to randomly select subjects from the whole population.
- Stratified sampling: Subsets of the data sets or population are created based on a common factor, and samples are randomly collected from each subgroup.
- Cluster sampling: The larger data set is divided into subsets (clusters) based on a defined factor, then a random sampling of clusters is analyzed.
- Multistage sampling: A more complicated form of cluster sampling, this method also involves dividing the larger population into a number of clusters. Second-stage clusters are then broken out based on a secondary factor, and those clusters are then sampled and analyzed. This staging could continue as multiple subsets are identified, clustered and analyzed.
- Systematic sampling: A sample is created by setting an interval at which to extract data from the larger population — for example, selecting every 10th row in a spreadsheet of 200 items to create a sample size of 20 rows to analyze.
Explain selection bias (with regard to a dataset, not variable
selection). Why is it important? How can data management procedures such as
missing data handling make it worse?
Selection bias is the phenomenon of selecting individuals, groups or data for analysis
in such a way that proper randomization is not achieved, ultimately resulting
in a sample that is not representative of the population.
Types of selection bias include:
- sampling bias: a biased
sample caused by non-random sampling
- time interval: selecting
a specific time frame that supports the desired conclusion. e.g. conducting a
sales analysis near Christmas.
- attrition: attrition bias is similar
to survivorship bias, where only those that ‘survived’ a long process are
included in an analysis, or failure bias, where those that ‘failed’ are only
- observer selection: related
to the Anthropic principle, which is a philosophical consideration that any
data we collect about the universe is filtered by the fact that, in order for
it to be observable, it must be compatible with the conscious and sapient life
that observes it.
Handling missing data can make selection bias worse because different methods impact the data in different ways. For example, if you replace null values with the mean of the data, you adding bias in the sense that you’re assuming that the data is not as spread out as it might actually be.
What is the difference
between type I vs type II error?
Anytime we make a decision using statistics there are four possible outcomes, with two representing correct decisions and two representing errors.
Type – I Error:
type 1 error is also known as a false positive and occurs when a researcher
incorrectly rejects a true null hypothesis. This means that your report that
your findings are significant when in fact they have occurred by chance.
The probability of making a type I error is represented by your alpha level (α), which is the p-value. A p-value of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis
Type – II Error:
type II error is also known as a false negative and occurs when a researcher
fails to reject a null hypothesis which is really false. Here a researcher
concludes there is not a significant effect, when actually there really is.
The probability of making a type II error is called Beta (β), and this is related to the power of the statistical test (power = 1- β). You can decrease your risk of committing a type II error by ensuring your test has enough power.
What are the four
main things we should know before studying data analysis?
Following are the key point that we should know:
- Descriptive statistics
- Inferential statistics
- Distributions (normal distribution / sampling
- Hypothesis testing
What is the difference between inferential statistics and descriptive statistics?
Descriptive Analysis – It uses the data to provide description of the
population either through numerical calculations or graph or tables.
Inferential statistics – Provides information of a sample and we need to inferential statistics to reach to a conclusion about the population.
How to calculate
range and interquartile range?
IQR = Q3 – Q1
Where, Q3 is the third
quartile (75 percentile)
Where, Q1 is the first quartile (25 percentile)
What is the benefit
of using box plot?
A box plot, also known as a box and whisker plot, is a type of graph that displays a summary of a large amount of data in five numbers. These numbers include the median, upper quartile, lower quartile, minimum and maximum data values.
Following are the advantages of Box-plot:
- Handle Large data easily – Due to the five-number data summary, a box plot
can handle and present a summary of a large amount of data. Organizing data in
a box plot by using five key concepts is an efficient way of dealing with large
data too unmanageable for other graphs, such as line plots or stem and leaf
- A box plot shows only a simple summary
of the distribution of results, so that it you can quickly view it and compare
it with other data.
- A box plot is a highly visually
effective way of viewing a clear summary of one or more sets of data.
- A box plot is one of very few
statistical graph methods that show outliers. Any results of data that fall
outside of the minimum and maximum values known as outliers are easy to
determine on a box plot graph.
What is the meaning of standard deviation?
how far are the data points from the mean
(σ) = √(∑(x-µ)2 / n)
Variance is the square of standard deviation
What is left skewed
distribution and right skewed distribution?
- The left
tail is longer than the right side
- The right
tail is longer than the left side
What does symmetric distribution mean?
The part of the distribution
that is on the left side of the median is same as the part of the distribution
that is on the right side of the median
Few examples are – uniform distribution, binomial distribution, normal distribution
What is the relationship between mean and median in normal distribution?
In the normal distribution mean is equal to median
What does it mean by bell curve
distribution and Gaussian distribution?
Normal distribution is called bell curve distribution / Gaussian distribution.It is called bell curve because it has the shape of a bell.It is called Gaussian distribution as it is named after Carl Gauss.
How to convert normal distribution to standard normal distribution?
Standardized normal distribution
has mean = 0 and standard deviation = 1. To convert normal distribution to
standard normal distribution we can use the formula
X (standardized) = (x-µ) / σ
What is an outlier? What can I do
outlier is an abnormal value (It is at an abnormal distance from rest of the
Following thing we can do with outliers
- When we
know the data-point is wrong (negative age of a person)
- When we
have lots of data
- We should
provide two analyses. One with outliers and another without outliers.
there are lot of outliers (skewed data)
results are critical
outliers have meaning (fraud data)
What is the difference between population parameters and sample statistics?
Population parameters are:
Sample statistics are:
How to find the mean length of all fishes in the sea?
Define the confidence level (most common is 95%). Take a sample of fishes from the sea (to get better results the number of fishes > 30). Calculate the mean length and standard deviation of the lengths. Calculate t-statistics. Get the confidence interval in which the mean length of all the fishes should be.
What are the effects of the width of confidence interval?
- Confidence interval is used for decision making
- As the confidence level increases the width of the confidence interval also increases
- As the width of the confidence interval increases, we tend to get useless information also.
Mention the relationship between standard error and margin of error?
As the standard error increases the margin of error also increases.
What is p-value and what does it signify?
The p-value reflects the strength of evidence against the null hypothesis. p-value is defined as the probability that the data would be at least as extreme as those observed, if the null hypothesis were true.
- P- Value > 0.05 denotes weak evidence against the null hypothesis which means the null hypothesis cannot be rejected.
- P-value < 0.05 denotes strong evidence against the null hypothesis which means the null hypothesis can be rejected.
- P-value=0.05 is the marginal value indicating it is possible to go either way.
How to calculate p-value using manual method?
- Find H0 and H1
- Find n, x(bar) and s
- Find DF for t-distribution
- Find the type of distribution – t or z
- Find t or z value (using the look-up table)
- Compute the p-value to critical value
What is the difference between
one tail and two tail hypothesis testing?
- Two tail test – When null hypothesis
contain an equality (=) or inequality sign (<>)
- One tail test – When the null
hypothesis does not contain equality (=) or inequality sign (<, >, <=,
What is A/B testing?
A/B testing is a form of hypothesis testing and two-sample hypothesis testing to compare two versions, the control and variant, of a single variable. It is commonly used to improve and optimize user experience and marketing.
What is R-squared and Adjusted R-square?
R-squared or R2 is a value in which your input variables explain
the variation of your output / predicted variable. So, if R-square is 0.8, it
means 80% of the variation in the output variable is explained by the input
variables. So, in simple terms, higher the R squared, the more variation is
explained by your input variables and hence better is your model.
However, the problem with
R-squared is that it will either stay the same or increase with addition of
more variables, even if they do not have any relationship with the output
variables. This is where “Adjusted R square” comes to help. Adjusted R-square
penalizes you for adding variables which do not improve your existing model.
Hence, if you are building
Linear regression on multiple variable, it is always suggested that you use
Adjusted R-squared to judge goodness of model. In case you only have one input
variable, R-square and Adjusted R squared would be exactly same.
Typically, the more non-significant variables you add into the model, the gap in R-squared and Adjusted R-squared increases.
Explain ANOVA and it’s applications?
Analysis of Variance (abbreviated as ANOVA) is an extremely useful technique which is used to compare the means of multiple samples. Whether there is a significant difference between the mean of 2 samples, can be evaluated using z-test or t-test but in case of more than 2 samples, t-test can not be applied as it accumulates the error and it will be cumbersome as the number of sample will increase (for example: for 4 samples — 12 t-test will have to be performed). The ANOVA technique enables us to perform this simultaneous test. Here is the procedure to perform ANOVA.
Let’s see with example: Imagine we want to compare the salary of Data Scientist across 3 cities of india — Bengaluru, Delhi and Mumbai. In order to do so, we collected data shown below.
Following picture explains the steps
followed to get the Anova results
There is a limitation of ANOVA that it does not tell which pair is having significant difference. In above example, It is clear that there is a significant difference between the means of Data Scientist salary among these 3 cities but it does not provide any information on which pair is having the significant difference
What is the difference between Correlation and Covariance?
Correlation and Covariance are statistical concepts which are generally used to determine the relationship and measure the dependency between two random variables. Actually, Correlation is a special case of covariance which can be observed when the variables are standardized. This point will become clear from the formulas :
Here listed key differences between covariance and correlation