Old Timer

My definition of "old-timer" would be someone in their 70s.80s. So I would take the age at which Mr. Old Timer was raising his children, which was apparently when he would have been at the age to determine respectfulness of his own children. Let 's say 25 years old. That generation would have had multiple children by then. If he was born in 1940, he would have been 25 years old in 1965. I would take data from his geographic area from the years 1965 and 2010. Each group would have 20 people who were under the age of twenty years old in each of the two years. They would be interviewed on criminal record, juvenile record, level of education, drug use or history of, and perceived life satisfactions during childhood, as well as parenting skills they feel are important.
Hypothesis: Children in 1960 were less likely to participate in risky behavior, and had more positive relationships with their peers, parents and other adults in 1965. Kids in modern day have more electronic entertainments that can change their behavior. I would test this experiment by trying to locate what year the old timer is referring to. Then I would start from that year and count the number of electronic entertainments introduced. Then I would find out how many electronic entertainments there in each family by making a survey. Next, I will take the average amount of electronic entertainments in each household, and compare the result to how many electronic entertainment devices in the old-timer 's years. I would use the T-test to compare the data.
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Suppose you hear an "old-timer" say, "Why, in my day, kids were much more respectful and didn 't cause as much trouble as they do nowadays!" Formulate a hypothesis related to this statement that you could test. How would you test it?
A hypothesis is important to formulate before we attempt to accept, reject, or simply not give credit to a statement or set of information. When a researcher formulates a hypothesis it 's

References: Schreiner, E.(2011). Types of Hypothesis Testing. Retrieved from http://www.ehow.com/info_12117436_types-hypothesis-testing.html   Chang, J. (2011). Statistical Analysis and Hypothesis Testing. Retrieved from http://www.ehow.com/video_12186015_statistical analysis  -hypothesis-testing.html The other thing with statistical hypothesis testing is that there can only be an experiment performed that doubts the validity of the null hypothesis, but there can be no experiment that can somehow demonstrate that the null hypothesis is actually valid. This because of the falsifiability-principle in the scientific method. Therefore it is a tricky situation for someone who wants to show the independence of the two events, like smoking and lung cancer in our previous example. This problem can be overcome using a confidence interval and then arguing that the experimental data reveals that the first event has a negligible (as much as the confidence interval) effect, if at all, on the second event. Read more: http://www.experiment-resources.com/statistical-hypothesis-testing.html#ixzz26KHlo8oE