6052 Discussion 9
AmblerchickChapter 17 Study Sheet
Inferential Statistics
Kori Martinez, Sally Snyder, Blossom Jose, Susan Hensley, & Chinyere Etufugh
Inferential statistics are based on the laws of probability. It is important for nurses to have a working knowledge of statistics to be able to understand and interpret data found in literature. Enhanced patient care depends on nursing having the knowledge to know how to interpret evidenced based data. Florence Nightingale first used statistics to improve sanitary conditions during the Crimean War (Giuliano & Polanowicz, 2008).
Description of Statistical Methods covered in chapter:
· Most hypothesis testing involves a twotailed test, in which both ends of the sampling distribution are used to define the region of improbable values; onetailed analysis may be appropriate if there is a strong rationale for an a priori directional hypothesis.
· Parametric tests involve the estimation of at least one parameter, the use of interval or ratiolevel data, and assumptions of normally distributed variables; nonparametric tests are used when the data are nominal or ordinal or when a normal distribution cannot be assumed.
· Test for independent groups compare separate groups of people, and dependent groups compare the same group of people over time or conditions.
· McNemar's test is used on nominal data in a contingency table with a dichotomous trait, with matched pairs of subjects, to determine whether the row and marginal column frequencies are equal (McNemar's Test, 2017).
Key statistical tests:
· ANOVA: Analysis of variance is the parametric procedure for testing the difference between means when there are 3 or more groups. This is used to find out if they need to reject the null hypothesis or accept the alternate hypothesis. An example is when a researcher wants to test different groups: for instance; a group of psychiatric patients is trying three different therapies: counseling, medication, and biofeedback, the researcher wants to see if one therapy is better than the others (Polit & Beck, 2017).
· Paired ttest: Obtaining two measurements from the same people from a paired set of participants. This measures the difference between two related groups. An example of this includes testing the success rates, adverse symptom rate, and mortality of new drugs.
· Chisquare: is used to test hypotheses about differences in proportions. Fisher’s exact test should be used for small samples.
Level of Measurement of Dependent Variable 
Group Comparisons:

Correlation analyses (to examine relationship strength 


2 Groups 
3+ Groups 



Independent Group Tests 
Dependent Group Tests 
Independent Group Tests 
Dependent Group Tests 

Nominal (categorical) 
X2 (or Fisher’s test) 
McNemar’s test 
X2Chisquare 
Cochran’s Q 
Phi coefficient or Cramer’s V 
Ordinal (rank) 
MannWhitney test 
Wilcoxon signed ranks test 
KruskalWallis H test 
Friedman’s test 
Spearmans’s rho (or Kendall’s tau) 
Interval or ration (continuous) 
Independent group ttest 
Paired ttest 
ANOVA 
RMANOVA 
Pearson’s r 

Multifactor ANOVA for 2+ independent variables 



RMANOVA for 2+groups x 2+ measurements over time 

References
Giuliano, K., & Polanowicz, M. (2008). Interpretation and use of statistics in nursing research.
AACN Advanced Critical Care, 19(2), 211222. doi:
10.1097/01.AACN.0000318124.33889.6e.
McNemar's Test. (2017). Retrieved from
http://www.statisticssolutions.com/nonparametricanalysismcnemarstest/
Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for
nursing practice (10th ed.). Philadelphia, PA: Wolters Kluwer.
Response 1 Week 9
Kori, Sally, Blossom, Susan, and Chinyere,
I appreciate your post; I especially enjoyed your use of your table. It definitely brought everything together. According to Polit and Beck (2017), the chisquare test enables us to test hypotheses about group differences in proportions by summarizing between observed and expected frequencies for each cell. For me your table laid the differences in each test a little more clear.
Reference
Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for
nursing practice (10th ed.). Philadelphia, PA: Wolters Kluwer.