HYPOTHESIS TESTING

  • A Hypothesis Test evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data.
  • A hypothesis is a tentative insight into the natural world; a concept that is not yet verified but if true would explain certain facts or phenomena.
  • HYPOTHESIS means assumption,supposition,estimation,presumption… It may be true or false.
  • Does training program T lead to improved staff efficiency?
  • Are the frequencies of large individual private motor insurance claims consistent with a lognormal distribution?
  • Alternative hypothesis: Contrary to the null hypothesis, the alternative hypothesis (denoted by H1 or Ha or HA) is the statement that the statistic has a value that somehow differs from the null hypothesis.
  • Level of significance: Refers to the degree of significance in which we accept or reject the null hypothesis. 100% accuracy is not possible for accepting or rejecting a hypothesis, so we, therefore, select a level of significance that is usually 5%.
  • Type I error: When we reject the null hypothesis, although that hypothesis was true. Type I error is denoted by alpha. In hypothesis testing, the normal curve that shows the critical region is called the alpha region.
  • Type II errors: When we accept the null hypothesis but it is false. Type II errors are denoted by beta. In Hypothesis testing, the normal curve that shows the acceptance region is called the beta region.
  • Power: Usually known as the probability of correctly accepting the null hypothesis. (1-beta) is called the power of the analysis.
  • One-tailed test: When the given statistical hypothesis is one value like H0: μ1 = μ2, it is called the one-tailed test.
  • Two-tailed test: When the given statistics hypothesis assumes a less than or greater than value, it is called the two-tailed test.
  • select a suitable statistical model
  • design and carry out an experiment/study
  • calculate a test statistic
  • calculate the probability value
  • determine the conclusion of the test
  • Test statistic for mean
  • Test statistic for variance using the corresponding formulas.
  • If the alternate hypothesis gives the alternate in only one direction (either less than or greater than) of the value of the parameter specified in the null hypothesis, it is called a One-tailed test.
  • Type II error — when we accept a false null hypothesis.
  • The level of confidence should be more than 95%. Less than 95% of confidence will not be accepted.
  • It is the probability of a type 1 error. It is also the size of the critical region.
  • If H0 is not rejected at a significance level of 5%, then one can say that our null hypothesis is true with 95% assurance.
  • If the p-value is smaller than alpha, we reject the null hypothesis.

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Bhanu Shahi

Bhanu Shahi

Data Analyst at Decimal Tech | Machine Learning | NLP | Time Series | Python, Tableau & SQL Expert | Storyteller | Blogger