verification of a statement’s validity

Reflection 2

Introduction

Main theme of the chapter is hypothesis testing (verification of a statement’s validity). It outlines the major types of hypothesis, the various methods applied in hypothesis testing, their implementation and which test is best for what type of hypothesis. The reading can be categorized into three parts.

Part one tries to provide a simple yet precise introduction and definition of hypothesis testing, which it does implicitly. It defines probability, from which the decision is based on. Different types of null hypothesis, those that test for differences and those that tests for relationships are also provided. The reader acquires the sense of reliability and accuracy of the decision made due to its clarity and simplicity (Carol, 2002).

In part two, relevancy is stressed by the provision of applications or practicability of hypothesis testing in real life situations. It does this by giving examples, for instance applications in scientific medical studies and in the education field. Therefore speculations and guess work are avoided and replaced by statistical analysis of data. Major decisions regarding expansion, elimination and modification are made with certainty.

Part three; the option of using either one tailed or two tailed tests provides confidence in the final result hence reliability. Each of which have strengths in different types of hypothesis. Clear steps of implementation are provided; these are given in simple language such that any interested person (not necessarily a statistician) can understand. The base for decision making is also provided. The impacts of the decision are carefully scrutinized before any further steps can be made. A test applied when there are only two groups with one independent variable, the t-test, is provided which uncovers even the slightest.

As is with any experiments, assumptions should be clearly prescribed to enhance efficiency. This is achieved by the reading; assumptions are laid out, which makes it easier to analyze data effectively. The assumptions in a t-test include: equal variability of the individual groups’ mean scores, both groups should hold equal number of elements, except for the dependent variable; all other variables should be equal. The vagueness of these assumptions makes it difficult to comprehend. They are too many to remember and one may easily confuse one with the other. The reading lacks the general definition of the independent and dependent variables, it only gives examples and leaves the decision to the reader. Steps involved in the implementation process are more or less the same for both hypothesis testing for scientific process and t-test. It’s quite difficult to distinguish between the two.

In my statistics class, the chapter is covered as one unit for the whole semester. Well, the topic is broad and has a vast area of applications; this made it difficult to understand. A teacher would give examples with no real life connection, the values provided would be numerous, too much time spend on a single subject rendering you tired within minutes. At the end of the class every student is left with a different level of understanding. This chapter does it for me in a couple of hours. It’s specific, the examples are practicable, no difficult mathematical terms, and the points arranged in good order. There is familiarity of the concept, but the degree of understanding between a class’s lecture and this chapter is different. You understand points and terms better.

Conclusion

Each test is discussed in detail with examples. The reading relies too much on definitions rather than the actual implementation. In the end, the reader not only understands the role of hypothesis testing in education process but also appreciates it. Statistics is a piece of cake.

References

Carroll, J. Rovezzi, S. (2002) .Statistics made simple for school leaders: data-driven

Decision making. Scarecrow Press. Pages 71-94.