Week 1: DQ1: What are the differences between descriptive and inferential statistics? According to Bennett (2009), the biggest difference between descriptive and inferential statistics is that descriptive statistics “deals with describing raw data in the form of graphics and sample of statistics” and inferential statistics “deals with estimating population parameters from sample data. ” This means that inferential statistics would be an estimate because the data would be estimated from sample data rather than using specific data whereas descriptive statistics would be more accurate.

An example of descriptive statistics would be trying to find an average of something such as a G. P. A. or your overall grade in a class. Inferential statics can be used to find the effectiveness of a new medication on a target group. References: Bennett, J. O. , Briggs, W. L. , & Trivola, M. F. (2009). Statistical reasoning for everyday life (3rd ed. ). Boston, MA: Pearson Education. DQ2: What are the four levels of management? The four levels of management are: The nominal level of measurement is the simplest level of variables such as hair color or gender.

Ordinal level of measurement is data with a ranking or ordering scheme such as a star rating used on movies. Interval level of measurement is when intervals are meaningful but ratios are not such as Fahrenheit temperatures. Ratio level of measurement is when intervals and ratios are both meaningful such as data consisting of distances. Data is classified into four levels of measurement so the information is easy to follow and research. The measurements help researchers keep data organized, this also helps to keep the measurements accurate. EBOOK COLLECTION: Bennett, J. O. , Briggs, W. L. , & Triola, M.

F. (2009). Statistical Reasoning for Everyday Life (3rd ed. ). Boston, MA: Pearson Education, Inc. Week 2 DQ1: The mean is a computation of numbers. To find the mean of a series of numbers we first add the numbers up and then divide that number by the amount of numbers you had to add up. For an example: 5+10+5+10+5= 35 then we divide 35/5 because we used 5 numbers to add. We come up with the number 7. 7 would be the mean in this case. “The median is the middle value of the data set. To find a median we arrange the values in ascending (or descending) order, repeating data values that appear more than once.

If the number of values is odd, there is exactly one value in the middle of the list, and this value is the median. If the number of values is even, there are two values in the middle of the list, and the median is the number that lies halfway between them. For an example the list 3, 4, 6, 6, 10. The median number is 6 because 6 is the middle number in the list. ” (Bennett, Briggs, & Triola, 2009, p. 146). “The mode is the most common value or group of values in a data set. For an example the mode in the number set 3, 4, 6, 6, 10 is 6 because this value occurs twice in the data set. (Bennett, Briggs, & Triola, 2009, p. 146). We would use mean, median and mode in healthcare to find the average of many things such as how effective a new medication would be on the average population. Or how far into a new treatment patients start seeing results or improvements. Bennett, J. O. , Briggs, W. L. , & Triola, M. F. (2009). Statistical Reasoning for Everyday Life (3rd ed. ). Boston, MA: Pearson Education Inc DQ2: Measures of variability are range, Interquartile range, variance, and standard deviation. Range is the difference between the highest and the lowest values in the data.

Interquartile range is the range that contains the middle 50% of numbers in a distribution. Variance is the average squared difference of the scores from the mean. Standard deviation is the square root of the variance or a measure of how widely data is spread around the mean of a data set (Bennett, Briggs, & Triola, 2009). Measures of variability are essential to inferential statistics because they provide more information on the data collected. The mean, median, and mode are not always sufficient in supporting the evidence found so these methods simply supply the supporting evidence. Bennett, J. O. , Briggs, W. L. , & Triola, M.

F. (2009). Statistical Reasoning for everyday life, Third Edition. Retrieved from https://ecampus. phoenix. edu/content/eBookLibrary2/content/eReader. aspx. Week 3 DQ1: Type I error is when the null hypothesis is wrongly rejected and this leads to wasting money trying to fix a process that isn’t broken. (Bennett, Briggs, ;amp; Triola, 2009). Type II error is when we wrongly fail to reject the hypothesis. (Bennett, Briggs, ;amp; Triola, 2009). Researchers need to be concerned with both types of errors because they can cause a patient mental and physical suffering if they think they are suffering from something that they are not.

Patients trust health care professionals and everything they say and when they tell us something we take it as accurate, for the most part, even when it is not what we want to hear. As far as I am concerned both errors are just as bad as the other. Both errors are going to cause some type of suffering on the patients behalf. If a patient is falsely diagnosed with terminal cancer they may lose hope and lead a reckless life because they feel they have nothing to lose. At the same time if a patient has cancer and is not diagnosed they will not get the treatment they need and may end up dying faster as a result.

Bennett, J. , Briggs, W. , ;amp; Triola, M. (2009). Statistical Reasoning for Everyday Life (3rd ed. ). Boston, MA: Addison-Wesley Person Educational, Inc.. DQ2: The difference between a null hypothesis and an alternative hypothesis is that a null hypothesis is always an equality whereas an alternative hypothesis is either greater than or less than but it cannot be equal. (Bennett, Briggs, ;amp; Triola, 2009). An example of a null hypothesis would be to say that 99% of cancer patients will die from their disease.

An alternative hypothesis would not say that all cancer patients will die from their disease but that they can also die from various other things such as pneumonia from a weakened immune system from radiation or chemotherapy. Bennett, J. O. , Briggs, W. L. , ;amp; Triola, M . F. (2009). Statistical reasoning for everyday life (3rd ed. ). Boston, MA: Pearson Education Week 4 DQ1: In Parametric statistical tests the data is “normally distributed”. This means, when graphed, the data follow a “bell shaped curve”.

Non-parametric statistical tests do not make an assumption about the distribution of data. They are better suited for situations where your data is skewed. (Graph Pad, n. d. ) Graph Pad. (n. d. ). Intuitive Biostatistics: Choosing a statistical test. Retrieved from http://www. graphpad. com/www/book/Choose. htm DQ2: According to Bennett, Briggs, and Triola ” The P-Value (probability Value) for a hypothesis test of a claim about a population parameter is the probability of selecting a sample at least as extreme as the observed sample, assuming that the null hypothesis is true (2009, p. 375). It means the likelihood of a statistical value being significant or that the result was less likely to occur purely by chance. 0. 05 shows that the sample result is unlikely resulting in the rejection of the null hypothesis. 0. 001 would have a low chance of being incorrect therefore 0. 05 would have more of a chance of being incorrect. Using these values in healthcare one can conclude that using the study with lower P values would be the safer option for the patient.

Bennett, J. O. , Briggs, W. L. , & Triola, M. F. (2009). Statistical Reasoning for Everyday Life (3rd ed. ). Boston. MA: Pearson Education, Inc.