# Basic Concepts of Hypothesis Testing (1)

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Basic Concepts of Hypothesis Testing Introduction A hypothesis is a statement or a tentative theory that may or may not be true, but is initially assumed to be true until new evidence suggests otherwise. It may be proposed from a preliminary observation, a guess or based from previous experiences. In hypothesis testing problem, the researcher has in mind a specific notion concerning the characteristics of the population under study before the sample data are gathered. Then investigate the sample information to examine how consistent the data with the hypothesis in questioned. If the sample information deviate much from the stated hypothesis, then researcher tend to disbelieved and reject the proposed statement. Although the proposed statement may be true, it is expected that any single sample (or samples)

will differ slightly from the true characteristic of the population and other will not, because of the sampling variation, have the same exact value as the population parameter. Hence, differences between the sample information and the population under study might be due chance. The procedure of statistical test will provide the basis in deciding whether differences between the sample observation and the hypothesized value could be due to sampling variation alone, or are so large enough as to make the proposed statement untenable. Types of Hypothesis Null hypothesis the null hypothesis is denoted by Ho, it is the hypothesis of no difference and usually formulated for the purpose of being rejected. Alternative hypothesis the alternative hypothesis is denoted by Ha or H1. This is the hypothesis that contradicts the hull hypothesis. If the null hypothesis is rejected, the

alternative is being supported. The alternative hypothesis is the operational statement of the experimenters research hypothesis. Types of Errors Type I error usually committed if the Ho is rejected when the Ho is true Type II error usually committed if the Ho is accepted when the Ho is false

In actual situation a given table below summarizes the kind of action and the type of error when one accepts or rejects a hypothesis. Status of Hypothesis Ho is True Ho is False One-sided and Two-sided Test Directional/Nondirectional Test In one-sided test the Ha specifies that the unknown test. While the nondirectional test. The following are classified as one-tailed test Ho : = 100 Ho : = 100 Ha : < 100 Ha : > 100 population parameter is entirely above or entirely below the specified value of the Ho. It is called one-tailed or a directional called two-tailed or Accept Ho Correct Decision Type II error Type I error Correct Decision Reject Ho

two- sided test, the Ha specifies that the unknown population

parameter can lie on either side of the value specified by Ho. It is

The Ha is either entirely below 100 or entirely above 100 The following are classified as two-tailed test Ho : = 100 Ho : 1 = 2 The Ha can fall in either side of the Ho Note : For a directional test the inequality symbol of the Ha is less than () while a non-directional test the symbol is not equal ( ). Ha : 100 Ha : (1 - 2) 0

Testing Level of Significance The probability of committing type I error is called the level of significance or margin of error of the test and it is denoted by (alpha), and one minus the level of significance is called confidence level. The probability of committing type II error is denoted by , and (1 ) is called the power of the test. This will indicate the ability of the test statistics to determine correctly that the Ho is false, hence it should be rejected. Rather compute the actual chance of committing type I error, the researcher conventionally establish the level of significance before hand by considering the consequences of committing type I error. There are 2 most commonly used level of significance 0.05 and 0.01. At 0.05 level, the researcher is willing to accept a 5% chance of being wrong decision when Ho is rejected. At 0.01 level, the researcher is willing to accept a 1% chance of being wrong when Ho is rejected. If Ho is rejected at 0.05 level, then it is usually labeled as "significant, otherwise the result is labeled not significant. If Ho is rejected at 0.01 level, then the result is labeled Highly significant. For a fixed sample size n, decreasing one type of error would mean increasing the other type of error. The only way to decrease both type of errors simultaneously is by increasing the sample size. The Critical Region The level of significance determines which values would be considered improbable or probable if the hypothesis were true. Thus, the range of possible values (sampling distribution) is divided into two sections or regions, the acceptance region (the probable values) and the rejection region (improbable values). The size of both region is completely specified by the level of significance. The acceptance region is equal to (1 ) and the critical region or the rejection region is equal to . The experimenter will decide to reject the null hypothesis only if the probability of observing of such an observed value is equal to or less than . The size of the critical region is being determined by , in general the location of the critical region is determined by the nature of the alternative hypothesis. The difference in the location of the critical region differentiates the statistical hypothesis into one-tailed or two-tailed test. Critical region is the set of all values of the test statistics that would cause to reject the null hypothesis. Basic Steps in Hypothesis Testing The following steps should be properly observed so as to make sure that the thinking is logical. 1. State the Ho and Ha, decide what data to collect and under what conditions 2. Specify the level of significance and the sample size n 3. Find the sampling distribution of the test statistics under the assumption that Ho is true 4. Establish the critical region for the test statistics 5. Computation of the test statistics, for a sample size n 6. Decision. Z-test Statistics Suppose that X1, X2, . . . Xn are sample observations from a normal populations with unknown mean and known variance. The appropriate test statistics in comparing the sample mean and the population is

which is normally distributed with mean 0 and variance 1. t-test Statistics

The population variance is generally unknown and is estimated by the variance of the random samples. The sampling variability of the sample variance may be affected if the sample size is small (n less than or equal to 30). Hence, the unavailability of the population variance must take into consideration and to be estimated by the sample variance. If the given observations X1, X2, . . ., Xn is a random sample from a normal distribution, but the population variance is unknown, then the test statistics

has a t-distribution with (n-1) degrees of freedom. A t-distribution is symmetric probability distribution centered at zero, and looks similar but more variable (spread out) than the normal distribution. The t-distribution becomes more and more similar with the normal distribution as the number of degree of freedom increases. Sample Problems 1. A study shows that the average score of the applicants who took the entrance examination was 45 with a standard deviation of 5.15. Is there a reason to believe that the present

examinees is better than the previous results if a random sample of 36 applicants showed an average score of 47.34, use 0.01 level of significance. ANSWER to Sample Problem 1 1. 2. 3. 4. 5. Ho : = 45 n = 36 Z-test Ha : > 45

= 0.01,

Test Statistics :

Critical Region : Reject the Ho if Zc > 2.33 Computations: (47.36 45) V36 Zc = = 2.75 5.15

6.

Decision: Since Zc > 2.33, therefore reject the Ho hypothesis and conclude that the new batch of applicants is better than the previous one.

2. A high school principal claims that the average performance of his graduating class in math was 83. To test this claim, 25 students were randomly selected from the recent graduating class with the following results: using 0.05 level of significance. ANSWER to Sample Problem 2 1. Ho : = 83 n = 25 t-test Ha : 83 87, 85, 76, 83, 78, 90, 89, 85, 82, 77, 79, 76, 86, 83, 93, 88, 84, 76, 79, 85, 82, 81, 85, 84, 80. Test the principal's claim

2. = 0.05,

3. Test Statistics :

4. Critical Region : Reject the Ho if tc > 2.064 5. Computations: X = 82.92 __ (82.92 83) V25 tc = = 2.75 4.6 6. Decision: Since tc < 2.064, therefore accept the Ho hypothesis and conclude that the claim of the principal is valid and the performance of new graduating class in Math is not S = 4.6 n = 25

significantly different from the previous graduates. Comparing Two Dependent Samples In comparing two dependent samples, the data are considered as a paired values. This is a result of data being obtained from a certain before and after studies, a result from a pairs of observation from two different populations, or a result from matching two subjects of similar characteristics to form a matched pairs. This pairs of observations are compared directly to one another by using their observed differences. The purpose of using correlated or dependent samples is to eliminate or remove the effect of uncontrolled factors which are not part but might influence the outcome of the study. Matched pairs or pairing of observations will assure the researcher that the observed differences of the two samples was really due to the influences of the factors under study. Test Statistics The difference between two populations when dependent or correlated samples was used is almost the same with the t-test with singles samples in the previous module. The only difference is that in dependent samples we will be dealing with the difference of two observed values rather than the original value

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