Guide To Basic Laboratory Statistics (Student's T-test) describes and demonstrates the statistical methods used in a laboratory experiment. Emphasis is on formulating a hypothesis and designing an experiment with one dependent variable and applying an appropriate statistical test. Documentation is provided, including definitions of terms, descriptions of each statistical test, diagrams for selecting the appropriate test with examples of each, and applets for applying the tests to the user's data.
Type of Material:
Tutorial
Technical Requirements:
JAVA applets must be supported.
Identify Major Learning Goals:
Appropriate use of parametric and nonparametric tests, including t-test, ANOVA, linear regression and correlation, Mann-Whitney, Kruskal-Wallis, and Spearman Rank Correlation is described through documentation and demonstrated through examples. Comparison of several tests on a single set of data is included.
Target Student Population:
The student needing to statistically analyze the results of a laboratory experiment is the intended audience. The nonparametric statistical tests used are beyond those normally used in an elementary statistics course. However, the emphasis is on the t-test, ANOVA and linear regression, all of which are included in introductory courses. The Spearman-Rank Correlation is explained with sufficient examples, so that the student could apply the test. The other distribution free tests were not emphasized.
Prerequisite Knowledge or Skills:
Prior knowledge of elementary statistics is important to be able to make sense of the various number levels that the tests return and to overcome the (unavoidable) "language barrier" that comes with the use of specialized terminology.
Content Quality
Rating:
Strengths:
Documentation is concise and thorough. Organization of topics with links to terms and appropriate tests make the site easy to use. Included applets for the statistical tests make it easy for the student to analyze his/her own data. Sample data can be modified by the user, and calculations quickly redone, which allows the student to discover the effects of the modification.
Concerns:
Definitions of discrete and continuous variables are not included in the documentation. All the examples of discrete variables are qualitative and those for continuous variables are quantitative. A quantitative example that is also discrete would help clarify the differences in the terms. Spearman Rank-Correlation was described in three places: in the flow chart as having factors of correlation rather than discrete or continuous, in the comparison charts as continuous and in the general description as either discrete or continuous.
Potential Effectiveness as a Teaching Tool
Rating:
Strengths:
This is an excellent tool for the user who is designing an experiment and selecting appropriate statistical tests for analysis of the results. Thorough documentation makes it difficult to select an inappropriate test.
Concerns:
Feature(s) of Concern and Recommendations:It should be pointed out more clearly that the reader needs to obtain background knowledge on the tests somewhere else. Also (see ease of use), while the site supports the selection of an appropriate test, it may be cumbersome to execute the test.
Ease of Use for Both Students and Faculty
Rating:
Strengths:
Use of links to definitions of terms is helpful to the user. Applets that include examples of each statistical method and allow the user to test his/her own data are excellent. A table of contents visible on the screen allows the user to effectively jump between topics.
Concerns:
The applets do not allow for the loading and saving of data sets,
which limits their use to small data sets and training situations. Users that will need to use other tools to perform their tests will ultimately rely on those tools and abandon this site.
Creative Commons:
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