LEY PARA LA EQUIDAD TRIBUTARIA EN EL ECUADOR Título Primero

In some cases at times, there may be need to draw inferences about differences between two or more populations. But you should note that in an experimental research, the scientist may have two groups, an experimental group and a control group, and may wish to test if there is any difference between the two groups. For example, a chemist may wish to check if two types of solutions have different degree of

Page | 78 acidity, the agriculturist may wish to test the effect of fertilizer on crop yield and compare yields from a plot treated with the fertilizer and another plot treated without the fertilizer. The decision taken will be based on the test of hypothesis known as the students test given by:

Where the degrees of freedom is . Note that are the respective sample means of the two groups. 𝑆x and 𝑆Y are the standard deviation, 𝑁X and 𝑁Y are the sample size of the two groups. The above formula is used when the population variance is known.

atr distribution where r, the adjusted degrees of freedom is determined by the equation:

If r doesn't equal an integer, as it usually doesn't, then we take the integer portion of r. That is, we use [r]

if necessary.

With that now being recalled, if we're interested in testing the null hypothesis: H0: μX = μY or μX − μY = 0

Illustration

In an econometrics examination, the 22 males used in the study has a mean score of 81 and a variance of 12 while the 20 females used has a mean score of 78 and a variance of 10. Do you think gender have an effect on the score of these university students at ∝=0.05and ∝=0.01?

Solution:

Page | 79 𝐻0:𝜇X= 𝜇Y of no difference and 𝐻1:𝜇X≠𝜇Y of difference

Let 𝑁X=22, 𝑁Y=20, 𝑆X=12, 𝑆Y=10, =81, =78, Using the formula:

Degree of freedom = (22+10) – 2 = 40.

From the t-table, using two-tailed test, (since it is a non-directional hypothesis) tabulated value for 40 degrees freedom is 2.02.Since the calculated value is greater than the tabulated value, we conclude that there is a difference in performance of males and females, with females scoring higher. At 0.01 level the tabulated is 2.70 which is less than the calculated, so we still reject the hypothesis.

4.0 CONCLUSION

Hypothesis testing has been seen as the way forward to justify the truth and false of a research situation or problem. So at this end, I believe you must have gained enough on how we formulate hypothesis.

This unit has look at the statistical test for hypothesis, and we can conclude that hypothesis testing is a process by which an analyst tests a statistical hypothesis. The methodology employed by the analyst depends on the nature of the data used, and the goals of the analysis. The goal is to either accept or reject the null hypothesis. We also looked at one and two tailed test. However, the unit analyses that in statistical significance testing, a one-tailed test or two-tailed test are alternative ways of computing the statistical significance of a data set in terms of a test statistic, depending on whether only one direction is considered extreme (and unlikely) or both directions are considered equally likely.

5.0 SUMMARY

This unit has vividly taken a look at the meaning of hypothesis and how it is used in solving some research problems. However, type 1 and type 2 errors were also examined to know whether we have accepted or rejected what we do not need to accept or reject. One and two tailed test has be discussed extremely in this unit and their graphs was also examined. Therefore we can conclude that one-tailed tests are used for asymmetric distributions that have a single tail, such as the chi-squared distribution, which are common in measuring goodness-of-fit, or for one side of a distribution that has two tails, such as the normal distribution, which is common in estimating location; this corresponds to specifying a

Page | 80 direction. Two-tailed tests are only applicable when there are two tails, such as in the normal distribution, and correspond to considering either direction significant. However, we have examine various aspect of statistical test for hypothesis such as procedure for carrying out tests for hypothesis, statistical test for mean of a single population when population variance is known and unknown and interval estimation for mean of a single population. Therefore, it is at this point that I believe that you must have learnt a lot from the unit.

6.0 TUTOR MARKED ASSIGNMENT

A psychologist was interested in exploring whether or not male and female college students have different driving behaviours. There were a number of ways that she could quantify driving behaviours.

She opted to focus on the fastest speed ever driven by an individual. Therefore, the particular statistical question she framed was as follows: Is the mean fastest speed driven by male college students different than the mean fastest speed driven by female college students? She conducted a survey of a random n = 34 male college students and a random m = 29 female college students. The results of her survey shows that the mean fastest speed driven by male and college students are 105.5 and 90.9 and their standard deviation are 20.1 and 12.2 respectively.

Is there sufficient evidence at α = 0.05, level to conclude that the mean fastest speed driven by male college students differs from the mean fastest speed driven by female college students?