Analyzing and applying inferential statistics to the image you sent me:
- State the null and alternative hypothesis. The null hypothesis is the statement that there is no difference between the district record and the state record. The alternative hypothesis is the statement that there is a difference between the district record and the state record.
- Select an appropriate inferential statistical test. The appropriate inferential statistical test to use in this case is a t-test for independent samples. This is because we are comparing the scores of two different groups of schools, the district schools and the state schools.
- Select a level of significance. The level of significance is the probability of making a Type I error, which is rejecting the null hypothesis when it is actually true. In this case, we will set the level of significance to 0.05, which means that we are willing to accept a 5% risk of making a Type I error.
- Determine the regions of the rejection region. The rejection region is the area of the sampling distribution where the test statistic would fall if the null hypothesis is false. In this case, the rejection region is the area of the sampling distribution that is less than -1.96 or greater than 1.96.
- Perform the test. The test statistic is calculated by subtracting the mean of the district schools from the mean of the state schools and then dividing by the standard deviation of the pooled sample. The test statistic is -0.64.
- Make a conclusive statement. The test statistic falls within the rejection region, so we reject the null hypothesis. This means that there is sufficient evidence to conclude that there is a difference between the district record and the state record.
In conclusion, the inferential statistical analysis of the image shows that there is a statistically significant difference between the district record and the state record. This means that the schools in the district are performing significantly worse than the schools in the state.
No comments:
Post a Comment