Day 9 - Hypothesis, Hypothesis Testing

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       By Jerin Lalichan 


Hypothesis

    A hypothesis is an educated guess about something you can test through observation or experimentation.
    Examples of hypotheses are: a new medicine you think might work, sleeping for 8 hours might improve memory power, drinking coffee before bed may cause delayed sleep, etc. It can be anything that can be tested.

Hypothesis Statement :

    This is how we represent a hypothesis. It contains an 'if ' condition and a  'then' statement.

eg: If I (give exams at noon instead of 7) then (student test scores will improve).

Hypothesis Testing




    This is a way of testing the outcome of a survey of experiments to see if they are reliable and are of meaningful results. Basically, we are finding the odds that our results have happened by chance. And if the results are happened by chance, it is useless or of little use, since it is not replicable.

Steps :
  • Figure out the null hypothesis,
  • State the null hypothesis
  • Choose what kind of test you need to perform
  • Either support or reject the null hypothesis

Null Hypothesis 

    The null hypothesis, H0 is the commonly accepted fact. It is the opposite of the alternate hypothesis. Researchers work to reject, nullify or disprove the null hypothesis. Researchers come up with an alternate hypothesis, which will explain the phenomenon, and then work to reject the null hypothesis.

Alternate Hypothesis

    An alternative hypothesis is a theory that opposes the null hypothesis, and an alternative hypothesis takes a different stance from the null. For example, if the null hypothesis estimates something to be true, then the alternative hypothesis estimates it to be false. 
    The alternative hypothesis often is the statement you test when attempting to disprove the null hypothesis. If you can collect enough data to support the alternative hypothesis, then it will replace the null hypothesis.


   
  I am doing a challenge - #66DaysofData  in which I will be learning something new from the Data Science field for 66 days, and I will be posting daily topics on my LinkedIn, On my GitHub repository, and on my blog as well.


Stay Curious!  





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