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Tests of significance

Tests of significance. Confidence intervals are used when the goal of our analysis is to estimate an unknown parameter in the population. A second goal of a statistical analysis is to verify some claim about the process on the basis of the data.

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Tests of significance

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  1. Tests of significance Confidence intervals are used when the goal of our analysis is to estimate an unknown parameter in the population. A second goal of a statistical analysis is to verify some claim about the process on the basis of the data. A test of significance is a procedure to assess the truth about a hypothesis using the observed data. The results of the test are expressed in terms of a probability that measures how well the data support the hypothesis.

  2. Example: How much does a dial-up connection cost? A consumer organization claimed that people paid $20 per month for Internet access. A study was conducted to check the truthfulness of this claim. Data from 50 users of commercial Internet service providers were collected in August 2000. These are the summary statistics computed in Excel – Data analysis toolpak Internet Fees Mean 20.900 Standard Error 1.081 Median 20.000 Standard Deviation 7.646 Minimum 8.000 Maximum 50.000 Sum 1045.000 Count 50.000 Confidence Level(95.0%) 2.173 Margin of error Thus the 95% C.I. Is 20.9 ± 2.173

  3. Statistical test The researcher would like to show that the unknown average (internet fees) is not equal to a specific value ($20) Hypothesis testing for one sample problem consists of three parts: I) Null hypothesis (Ho): The unknown average II) Alternative hypothesis (Ha): The unknown average is not equal to This is the hypothesis that the researcher would like to validate and it is chosen before collecting the data! There are three possible alternative hypotheses – choose only one! • Ha:  one-sided test • Ha:  two-sided test • Ha:  one-sided test

  4. III) The sample statistic computed from the data If the null hypothesis Ho is true (the sample mean is equal to the null value ) then the t statistic is approximately t-distributed with n-1 degrees of freedom, whenever the distribution of the data is symmetric. If n is large (n>30) then the t statistic is approximately standard normal under the null hypothesis Ho. We can use the distribution of the t statistic to measure how well the null hypothesis explains the observed value t*!

  5. Example The researcher wants to test if people paid on average a fee different from $20. Ho: = 20 null hypothesis Ha:  alternative hypothesis The test statistic is computed as How likely is the observed value of t* if the null hypothesis were true? probability = TDIST(0.832, 49, 2) = 0.409131

  6. The answer to the question is given by computing the test p-value. The p-value is the probability that the test statistic can be more extreme than its observed value. Two-sided test: If the alternative hypothesis is Ha: then the p-value is equal to the sum of the areas to the left of – t* and to the right of t*. -t* t* In Excel: p-value “=TDIST(ABS(t),df,2)” where df=n-1

  7. One-sided test • If the alternative hypothesis is • Ha: then the p-value is equal to • the area on the right of t* • In Excel: if t*>0 p-value “=TDIST(t*, df, 1)” for df=n-1 • if t*<0 p-value “=1-TDIST(ABS(t*),df,1)” • Ha: then the p-value is equal to • the area on the left of t* • In Excel: if t*<0 p-value “=TDIST(ABS(t*), df, 1)” for df=n-1 • if t*>0 p-value “=1-TDIST(t*,df,1)” P-value t* P-value t* These are called one-sided hypotheses, because they state that the true value is larger (1) or smaller (2) than the hypothesized value in H0.

  8. Significance levels • In common statistical terminology: • If p-value < 0.05, then the null hypothesis is rejected at 5% significance level and the test result is called “statistically significant”. • If p-value <0.01, then the null hypothesis is rejected at 1% significance level and the test result is called “highly significant”. • If p-value>= 0.05 then we can’t reject the null hypothesis, and the test result is “not significant”. • Notice that the significance levels are very popular for reporting the test results. However, it is better practice to summarize the test results reporting what test was used, the P-value and whether the test was “statistically significant” or “highly significant”.

  9. Making a test of significance • Follow these steps: • Set up the null hypothesisH0– the hypothesis you want to test. • Set up the alternative hypothesisHa– what we accept if H0 is rejected • Compute the value t* of the test statistic. • Compute the observed significance level P. This is the probability, calculated assuming that H0 is true, of getting a test statistic as extreme or more extreme than the observed one in the direction of the alternative hypothesis. • State a conclusion. You could choose a significance level. If the P-value is less than or equal to , you conclude that the null hypothesis can be rejected at level , otherwise you conclude that the data do not provide enough evidence to reject H0. • Warning! You can never prove a hypothesis. You can only show that its converse is highly unlikely.

  10. TDIST(x,df,tails) x   is the positive numeric value at which to evaluate the distribution. df  is an integer indicating the number of degrees of freedom. Tails specifies the number of distribution tails to return: If tails = 1, TDIST returns the one-tailed distribution p(T>x) If tails = 2, TDIST returns the two-tailed distribution P(T<-x)+P(T>x)=2P(T>x) x -x x

  11. Suppose that the researcher wanted to test if people paid a fee different than 20 dollars for Internet dial-up service. The test hypotheses are: The test statistic is the same: The p-value is the probability P(T<t*) =1-TDIST(0.832, 49, 2) =IF(p<0.05,”Reject Ho”,”Do not reject Ho”) What would a 1-tailed test look like? How can we achieve a lower p-value?

  12. The IF function IF(logical_test,value_if_true,value_if_false) Logical_test   is any value or expression that can be evaluated to TRUE or FALSE. Value_if_true   is the value that is returned if logical_test is TRUE. Value_if_false   is the value that is returned if logical_test is FALSE. For example: IF(A10<=100,"Within budget","Over budget") Returns “within budget” if cell A10 is less or equal to 100, otherwise the function displays "Over budget". In the example, IF(A10=100,SUM(B5:B15),““) if the value in cell A10 =100, then logical_test is TRUE  the total value for the range B5:B15 is calculated. Otherwise, logical_test is FALSE  empty text (“”) is returned (equivalent to a blank cell).

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