## Inferential Statistics

• Descriptive Statistics describes data, where as inferential statistics allows us to make predictions (inferences) from that data.
• With inferential statistics we take data from samples and make generalizations about population.
• For example:
• We might stand in a mall and ask a sample of 100 people if they like shopping at ‘Shoppers Stop’.
• With Descriptive Statistics
• We could make a bar chart/pie chart with yes or no aswers
• With inferential statistics
• We could use our research to reason that around 65-70% of the population would like shopping at `shoppers stop`
• Distribution is one of the most important concept to understand in inferential statistics.

## Distributions

• When we speak about distributions, we mean probability distributions
• Definitions:
• A distribution is a function that shows the possible values for a variable and how often they occur.
• In Probability theory and statistics, a probability distribution is a mathematical function that stated, in simple terms can be thought of as providing the probabilities of occurance of different possible outcomes in an experiment
• Examples:
• Normal Distribution
• Students t-distribution
• Poisson Distribution
• Uniform Distribution
• Binomial Distribution
• Exponential Distribution
• Example: Lets try to understand the distribution by considering a scenario of rolling one dice  • Activity: Lets try to understand the distribution by considering a scenario of rolling two die’s and we need a distribution of sum of two dies  • Refer Here for the xlsx containing dice problems solution

## Normal Distribution

• The normal distribution is also known as Gaussian distribution or the Bell Curve.
• This is one of the most common distribution due to following reasons:
• It approximates wide variety of random variables
• Distribution of sample means with large enought sample sizes could be approximated to normal
• All computable statistics are elegant
• Heavily used in regression analysis
• Good track record
• Examples:
• Biology
• IQ tests
• Stock market information
• This distribution is represented as    • Keeping the standard deviation constant, the graph of a normal distribution with
• smaller mean would look the same way, but be situated to the left (brown)
• larger mean would look the same way, but be situated to the right (red) • Keeping the mean constant, a normal distribution with
• smaller deviation would be situated in same spot but have a higher peak and thinner tails (red)
• a larger standard deviation would be situated in the same spot but have lower peak & fatter tails (in brown) This site uses Akismet to reduce spam. Learn how your comment data is processed. 