The null hypothesis of a test always predicts no effect or no association between variables in statistical hypothesis testing, whereas the alternative hypothesis outlines your research prediction of an effect or relationship.
Null hypothesis is a statistical hypothesis that is used to reject the null hypothesis. The alternative hypothesis is a statistical hypothesis that allows us to accept or reject the null hypothesis.
A null hypothesis is a statistical hypothesis that states that no difference exists between specific features of a population (or data-generating process). A gambler, for example, would be curious about the fairness of a game of chance.
Make a positive statement out of the questions, such as there is a relationship (correlation studies) or a difference between the groups (experiment studies), and you have the alternative hypothesis.
The null hypothesis is useful because it may be used to determine whether or not two measurable events have a relationship. It can tell the user whether the outcomes are due to chance or are the consequence of controlling a phenomenon.
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1.easy to understand all topic
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plz provides all notes
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plzz provide notes....
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please provide notes also in pdf
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please provide course notes
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Shashi Kumar
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great resource to learn data science in hindi. but in this particular video lecture there is a mistake....actually mutually exclusive event can never be independent event.
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it really amazing to study....and easily understand difficult concepts...i hope you make more video on like power bi and nueral network model....its really helpful....thank you for these
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