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Inferential Statistics

There are several sampling techniques exist to make sure that the ‘Sample’ drawn from a population closely represents the population. If the ‘Sample’ is not representing the population close enough, the conclusion we establish about the population using the ‘Sample Drawn’ will not be correct. Hence care should be taken while selecting the sample from a population.

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Statistics

Statistics can be defined as the science of gathering, investigating, understanding and summarizing data. Statistics is applied in different domains to solve real-life problems – the domains include, industry, commerce, Education, Computer Science, Aviation, Banking, government operations, Research etc.

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Understanding p-value

P-Value is essentially deployed to validate a statistical hypothesis or in simpler terms it is used for hypothesis testing. P-value denotes the significance at which one can reject the null hypothesis drawn on a sample.

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Probability

Probability is a branch of mathematics which means the possibility or likelihood that a particular event will happen. Statistical analysis carried out by researchers is highly driven by the concepts in probability. Probability does not give certainty about happening of an event; it just predicts the possibility of an occurrence. Probability ranges between 0 and 1. Probability can be expressed as percentages by multiplying by 100 and the range is from 0% to 100%. The maximum likely value of any probability is 1. If the probability of an event is 1, the event is certain to happen. If the probability of an event is 0, then the event is very unlikely to happen. There are certain concepts to be discussed while understanding probability.

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Sampling

Sampling is the procedure by which a sample is selected out of the entire population. It enables to collect significant information about a population and is widely deployed in the field of Inferential Statistics.

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Sampling Error

Sampling error is defined as the error/bias occurred due to the selection of the particular sample instead of the entire population.

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Deterministic Model vs Probabilistic Model

In a Deterministic Model, the value of one or more attributes can be derived from other attributes.Given the distance travelled and the time taken for the travel, the speed can be calculated with a greater degree of accuracy.

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Box and Whisker Plot

A Box and Whisker plot or a Box Plot describes the famous five numbers or the famous five summary statistics of the data distribution : Median, First Quartile, Upper Quartile, Minimum Value, Maximum Value

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Descriptive Statistics Overview

Descriptive Statistics summarises whole data set and provide succinct information about the population. Remember, Descriptive Statistics “uses the whole population data” to provide a statistical summary through several parameters.

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Variables

A variable is a characteristic of a statistical observation, study, distribution thats takes any value from a set of infinite or finite values

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Discrete Variables vs Continuous Variables

Discrete Variables are variables which can take only finite/limited number of values. Discrete Variables generally do not follow a particular

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Fair Experiment

In the fields of Science and Research one would have often come across terms like a fair test, fair experiment, etc. which is an imperative for research

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