This week I learned about Inferential Statistics which is concerned with the conclusions of a population based on the sample. There are two types of inferential statistics and those are estimation and hypothesis testing. Estimation is about determining the values of unknown parameters while hypothesis testing is about the validity of assertions made on the parameters. There are two types of estimates: point estimate and interval estimate.  In hypothesis testing there are 7 steps to calculating the validity of a certain assumption. The first step is to find out what is the population parameter of interest which could be mean or proportion. The second step is to formulate the hypothesis there are two types of hypothesis that should be formulated the first is the Null hypothesis (Ho) this hypothesis always uses equal to, less than or equal to, or more than or equal to the second hypothesis to be formulated is the Alternate hypothesis (Ha) which the exact opposite of the Null hypothesis. An example of formulating the two is let's say that a group of people is accusing a company of  putting the promised amount of 50 grams in their packaging. Let's say that the population parameter of interest is mean the alternate hypothesis for this situation is Ha x̄ < M and the null hypothesis would be Ho = x̄ ≥ M. Because the hypothesis is less than it is one-tailed and this makes it directional. Step three is to check the assumptions one of those is: Is the population large enough? Step four is to choose the significance level for alpha which in most cases is mentioned in the problem. Step five is to select the appropriate test statistics there are two which are the z-test and the t-test in the example it would be suitable to use the z-test and the formula for it is z= x̄ -M/s√n .  Step 6 is to state the decision whether to reject the hypothesis or accept it. Step 7 is to compare the computed value to the critical value.