100 word response 1 reference/intext citation Due 8/11/2024
Laughlin
Differential vs Inferential Statistics:
Differential statistics are statistics we use to summarize and organize facts and data in a way that is meaningful to the project at hand. Descriptive statistics could be described and have been used going all the way back to middle school with mean, median and mode in graphs. In everyday life, differential statistics could look like how many graduates from Nova Southeastern University go on to get jobs in their field over the past 10 years. If you have kids, when they make birthday calendars that is also another real-life example. You then go into the calendar and make a box graph to show the statistics of what month has the most birthdays.
Inferential statistics take a sampling, or a smaller group. These can look like polls, the 100 people questioned for Family Feud, and look at the common pattern within those. Inferential statistics measure those patterns from the groups or controls to make generalizations about larger groups. If 85 out of 100 people like the color pink in the Atlanta area, it might be hypothesized that a 850 out of 1,000 people like pink over any other color. Almost all of the inferential statistics have an underlying assumption that is made.
In the program evaluation, inferential statistical tools can be used to monitor success or fail rates of specific programs within the At Promise Program such as the job help, GED program or sports programs. If those programs show to be failing statistically, how can the resources for that be allocated elsewhere if it is deemed not worth renewing. Descriptive statistics can show the average amount of money being used in specific locations throughout the program to evaluate if that is the best use. The graphics and statistics can also be used to measure the overall effectiveness of the program.
