Every student will face down the temptation to cheat on an assignment in his or her lifetime.
Statistics and statistical methods are of two basic types: Descriptive statistics summarize some facet of a complete population. They are used when an entire population is small or easy enough to measure. For example, the average height or weight of everyone in your family is a descriptive statistic.
Because all members of the population are included in the calculation, the result is a totally accurate, and thus completely reliable, measurement. Inferential statistics are used to predict or infer something about a very large population by measuring samples, or subsets, of that population.
This is done when it is virtually impossible, or prohibitively expensive, to obtain data about all members of a particular population. Many of the statistics we normally come in contact with while reading the paper, watching TV, or talking to colleagues are of the inferential variety.
Examples include the number of people projected to carry the HIV virus inthe average growth rate of maple trees, and the odds of incurring a side effect when taking a new drug. These types of statistics are thus used to make far-reaching policy decisions regarding everything from the number of street lights needed per city block, to the level of funding allocated to school lunch programs, to the amount of money spent to protect the grizzly bear population of the Western United States.
Thus, it is critical that we develop a good understanding of how best to use, and Stats term paper abuse, inferential statistics. Proportion The concept of proportion allows us to compare relative differences in size, quantity, etc.
The emphasis is on relative, because we don't know the absolute difference or the magnitude of that difference. Proportion can be measured as a If there are 10 girls and 5 boys in the choir, the ratio of girls to boys is 2 to 1. Percentages also play a major role when trying to determine the "odds" that an event might happen.
If it is discovered that 7 out of 10 mice are brown, we can infer that a. This idea leads us nicely into the concept of Probability When it comes to statistics, the term probability is used to describe Sample Size Generally speaking, the larger the sample size, the higher the level of probability that the statistics actually mean what they say they mean.
For example, using a sample of 10 people to draw inferences about the behaviors of a million people is not a smart thing to do, since there is only a small probability that the sample is representative of the whole population. However, a sample of may be more than adequate to allow for statistically sound inferences.
Watch out when samples that are of reasonable size become subdivided. This situation occurs fairly frequently when analyzing survey results, as there is a tendency to learn as much as possible about very specific but usually very small sub-samples.
This type of analysis is usually referred to as a "cross-tabulation. Say that out of people asked about their eating habits, 15 indicate that they are vegetarians. So far, so good. We only asked 15 people, a sample size that is far too small to give a reliable result.
To make this claim, we would have to start again by asking the question of enough vegetarians at least that we could be reasonably sure that the answer we received was projectable to all vegetarians.
Randomness All samples are not created equal. Generally, the fairest way to generate a sample is to do so randomly, letting the laws of probability spin their magic. Random samples of a large enough size will do a surprisingly nice job of modeling a large population.
Sometimes non-random samples make sense, especially when trying to draw inferences about specific population sub-groups e. But be careful of their use, especially when reviewing advertising claims.
The use of non-random samples to "stack the deck" is a favorite trick of unscrupulous advertisers. A car manufacturer will claim in big letters that its new model is preferred over a competing model. It's only in the small print that we find out that the study was conducted among a specific sample: First time buyers over the age of Thus, the results are projectable only among the portion of the population that has never bought a car but has filed for Social Security.
Reliability When working with inferential statistics, it is important to remember that the numbers are approximations of what the total population is like, not numbers describing the total population itself unless, of course, the sample IS the population.Decorated with red, white, blue, and baseballs, these baseball card templates allow kids to enter their own picture and stats.
Free to download and print. Internet World Stats, Population and Internet Users in all countries and usage in all regions of the world. The Internet Big Picture.
Who is most likely to be affected by long-term unemployment? South Africa's official unemployment rate is on the increase. In the past 10 years (–), the . View Essay - Term Paper Statistics from STATISTICS at Monroe College. MG Research & Statistics for Manager Decision Making Industry Analysis Paper 75%(8).
"Paper in Fire" is a song by American rock singer John Mellencamp, released as the first single from his ninth studio album The Lonesome Jubilee.
Notable Smaller-Scale Surveys and Scandals. Survey by David Wangaard and Jason Stephens of over 3, students in six New England-area high schools found .