Hypothesis Testing

• Hypothesis - general statement regarding certain description about the subject under consideration

  -it is an assumption

  -a conjecture

  -or inferential statements

  • it is a statement of claim or assertion about a population parameter or a casual relationship among group of subjects used in the merits of sample information
• Hypothesis - an assumption or inferential statement about the characteristics of the subject under consideration
• null hypothesis (H0) - the prevailing assumption. It suggest that there is no significant difference in the quantitative characteristics of the population
• alternative hypothesis (H2) - the new assumption. It implies the idea that there is a significant difference in the quantitative characteristics of the population
• Type 1 error - rejection of the null hypothesis when its true
• Type 2 error -Acceptance of the null hypothesis when its false
• a Claim from a statistical hypothesis test is the statement that we want to prove
• Level of Significance - is the probability of obtaining a type 1 error

  -it is the region where the population parameter does not fall within the confidence level , and the alternative hypothesis is accepted
• One tailed Hypothesis - The critical region for the alternative hypothesis lied in the left tail to the distribution of the test statistic, where the critical region for the alternative hypothesis lies entirely in the right tale
• Two-Tailed Test - the critical region is split into two parts, often having probabilities placed in each tail of the distribution of the statistic
• Critical Region - in a normal distribution, the data distribution is said to be symmetric , the critical value must be established and the rejection region for null hypothesis must identify and shaded
• Computation - compute the value of the appropriate test statistic from the sample data.
• Decision - Reject the null hypothesis if the test statistic has value in critical region, otherwise do not accept null hypothesis