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1Presenter: Shib Sekhar Datta Moderator: M S Bharambe Test of SignificancePresenter: Shib Sekhar DattaModerator: M S Bharambe
2Framework of presentation Introduction to test of significanceABCDEFGIJKLReferences
3Normal curveBecause a two-sided test is symmetrical, you can also use a confidence interval to test a two-sided hypothesis.In a two-sided test, C = 1 – α.C confidence levelα significance levelα /2α /2
4Tests of Significance Is it due to chance, or something else? This kind of questions can be answered using tests of significance.Since many journal articles use such tests, it is a good idea to understand what they mean.
5Why should we test significance? We test SAMPLE to draw conclusions about POPULATIONIf two SAMPLES (group means) are different, can we be certain that POPULATIONS (from which the samples were drawn) are also different?Is the difference obtained TRUE or SPURIOUS?Will another set of samples be also different?What are the chances that the difference obtained is spurious?The above questions can be answered by STAT TEST.
6Issues in significance tests Testing many hypothesesOne-tailed or two-tailed?A statistically significant result may not be importantSample sizeIf chance model is wrong, test result can be meaninglessA significant test does not identify the cause
7Four Key Points: Repeated Samples Plotting the means of repeated samples will produce a normal distribution: it will be more peaked than when raw data are plotted (as shown in Figure 9.1)
8Four Key Points (cont’d) The larger the sample sizes, the more peaked the distribution and the closer the means of the samples to the population mean (shown in Figure 9.2)
9Four Key Points (cont’d) The greater the variability in the population, the greater the variations in the samplesWhen sample sizes are above 100, even if a variable in the population is not normally distributed, the means will be normally distributed when repeated samples are plottedE.g., weight of population of males and females will be bimodal, but if we did repeated samples, the weights would be normally distributed
10One- and Two-Tailed Tests If the direction of a relationship is predicted, the appropriate test will be one-tailedIf the direction of the relationship is not predicted, use a two-tailed testExample:One tailed: Females are less approving of violence than are malesTwo-tailed: There is a gender difference in the acceptance of violence[Note: No prediction about which gender is more approving]
11Statistical testHypothesis testing for one sample problem consists of three parts:Null hypothesis (Ho): The unknown average is equal to sample meanII) Alternative hypothesis (Ha): The unknown average is not equal toThis 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 testHa: two-sided testHa: one-sided test
12Five Percent Probability Rejection Area: One- and Two-Tailed Tests
13Significance doesn’t rule out 100% Chance When a result is statistically significant, there is a 5% chance of obtaining something as extreme as, or more extreme than your observation, under the null hypothesis.Even if nothing is happening (the null hypothesis is true), you will get 5 significant results in 100 tests, just by chance.
14One-tailed vs Two-tailed Checking if the sample average is too big or too small before making an alternative hypothesis is a form of data snooping.A coin is tossed 100 times, and there are 61 heads. The null hypothesis says the coin is fair, so EV = 50, SE = 5, and z = (61 – 50) / 5 = 2.2.If the alternative hypothesis says the coin is biased towards the heads, then the P-value is roughly the area under the normal curve to the right of 2.2: 1.4%.
15If the alternative hypothesis is that the coin is biased, but this bias can be either way, then the P-value should be the area to the left of 2.2 and right of 2.2: 0.7%.A one-tailed test is OK if it was known before the experiment that the coin is either fair or biased towards the heads.If a reported test is two-tailed, and you think it should be one-tailed, just double the P-value.
16Suppose an investigator use z-test with two-tailed test and alpha at 0 Suppose an investigator use z-test with two-tailed test and alpha at 0.05 (5%) and gets z = 1.85, so P ≈ 6%. Most journals won’t publish this result, since it is not “statistically significant”.The investigator could do a better experiment, but this is hard. A simple way out is to do a one-tailed test.
17Was the result important? To compare WISC vocabulary scores for big-city and rural children, investigators took a simple random sample of 2,500 big-city kids, and an independent simple random sample of 2,500 rural kids. Big-city: average = 26, SD = 10. Rural: average = 25, SD = 10.Two-sample z-test. SE for difference ≈ 0.3, z = 1 / 0.3 ≈ 3.3, P = 5 / 10,000.The difference is highly significant, and the investigators use this to support a proposal to pour money into rural schools.
18The z-test only tells us that the difference of 1 point is very hard to explain away with chance variation. How important is this difference?
19P-value and sample size The P-value of a test depends on the sample size.With a large sample, even a small difference can be “statistically significant”, i.e., hard to explain by the luck of the draw. This doesn’t necessarily make it important.Conversely, an important difference may not be statistically significant if the sample is too small.
20The Role of the Chance Model A test of significance asks the question, “Is the difference due to chance?”But the test cannot be done until the word “chance” has been given a precise meaning.
21Does the difference prove the point? In an experiment of throwing a die: a die is rolled, and the subject tries to make it show 6 spots.In 720 trials, 6 spots turned up 143 times. If the die is fair, and the subject has no bias, expect 120 sixes, with SD(Hypothetical) 10.z = (143 – 120) / 10 = 2.3, P ≈ 1%.
22A test of significance tells you that there is a difference, but can’t tell you the cause of the difference.A test of significance does not check the design of the study.
23How to test statistical significance? State Null hypothesisSet alpha (level of significance)Identify the variables to be analyzedIdentify the groups to be comparedChoose a testCalculate the test statisticIf necessary calculate degrees of freedomFind out the P valueCompare with alphaDecision on hypothesis fail to reject or rejectCalculate the CI of the differenceCalculate Power if required
24How to interpret P?If P < alpha (0.05), the difference is statistically significantIf P > alpha, the difference between groups is not statistically significant / the difference could not be detected.If P> alpha, calculate the powerIf power < 80% - The difference could not be detected; repeat the study with deficit number of study subjects.If power ≥ 80 % - The difference between groups is not statistically significant.
25Statistical Significance The null hypothesis: Dependent and independent variables are statistically unrelatedIf a relationship between an independent variable and a dependent variable is statistically non-significantNull hypothesis is trueResearch hypothesis is rejectedIf a relationship between an independent variable and a dependent variable is statistically significantNull hypothesis is falseResearch hypothesis is supported
26Tests of Significance Hypotheses In a test of significance, we set up two hypotheses.The null hypothesis or H0.The alternative hypothesis or Ha.The null hypothesis (H0)is the statement being tested.Usually we want to show evidence that the null hypothesis is not true.It is often called the “currently held belief” or “statement of no effect” or “statement of no difference.”The alternative hypothesis (Ha) is the statement of what we want to show is true instead of H0.The alternative hypothesis can be one-sided or two-sided, depending on the statement of the question of interest.Hypotheses are always about parameters of populations, never about statistics from samples.It is often helpful to think of null and alternative hypotheses as opposite statements about the parameter of interest.
27Tests of Significance Test Statistics A test statistic measures the compatibility between the null hypothesis and the data.An extreme test statistic (far from 0) indicates the data are not compatible with the null hypothesis.A common test statistic (close to 0) indicates the data are compatible with the null hypothesis.
28Tests of Significance Significance Level (a) The significance level (a) is the point at which we say the p-value is small enough to reject H0.If the P-value is as small as a or smaller, we reject H0, and we say that the data are statistically significant at level a.Significance levels are related to confidence levels through the rule C = 1 – αCommon significance levels (a’s) are0.10 corresponding to confidence level 90%0.05 corresponding to confidence level 95%0.01 corresponding to confidence level 99%
29Tests of SignificanceSteps for Testing a Population Mean (with s known)1. State the null hypothesis:2. State the alternative hypothesis:3. State the level of significanceAssume a = 0.05 unless otherwise stated4. Calculate the test statistic
30Steps for Testing a Population Mean (with s known) 5. Find the P-value:For a one or two-sided test:T testConsult t distribution table and get standard value for specific degree of freedomZ testN need to consult table as it is independent of degrees of freedomAt 5% level of significance Z=1.96At 1% level of significance Z =2.58
31Steps for Testing a Population Mean (with s known) 6. Reject or fail to reject H0 based on comparison of test statisticIf the P-value is less than or equal to a, reject H0.It the P-value is greater than a, fail to reject H0.7. State your conclusionYour conclusion should reflect your original statement of the hypotheses.Furthermore, your conclusion should be stated in terms of the alternative hypothesesFor example, if Ha: μ ≠ μ0 as stated previouslyIf H0 is rejected, “There is significant statistical evidence that the population mean is different than m0.”If H0 is not rejected, “There is not significant statistical evidence that the population mean is different than m0.”
32Example : Arsenic“A factory that discharges waste water into the sewage system is required to monitor the arsenic levels in its waste water and report the results to the Environmental Protection Agency (EPA) at regular intervals. Sixty beakers of waste water from the discharge are obtained at randomly chosen times during a certain month. The measurement of arsenic is in nanograms per liter for each beaker of water obtained.”(From Graybill, Iyer and Burdick, Applied Statistics, 1998).Suppose the EPA wants to test if the average arsenic level exceeds 30 nanograms per liter at the 0.05 level of significance.
33Making a test of significance Follow these steps:Set up the null hypothesis H0– the hypothesis you want to test.Set up the alternative hypothesis Ha– what we accept if H0 is rejectedCompute 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.
34Examples of Significance Tests Arsenic ExampleInformation given:37.618.104.22.168.515.720.781.337.515.410.68.322.214.171.1241.140.63519.438.820.98.659.26.22433.821.615.36.687.74.810.7182.217.615263.546.917.4126.96.36.199.212128.523.5188.8.131.526.533.225.633.612.29.914.530Sample size: n = 60.Assume it is known that s = 34.
35Arsenic Example 1. State the null hypothesis: 2. State the alternative hypothesis:3. State the level of significancefrom “exceeds”a = 0.05 for one tailed test
36Tests using the z-statistic are called z-tests. Z Test StatisticTests using the z-statistic are called z-tests.
37Observed Significance Level z is the number of SEs an observed value is away from its expected value, based on the null hypothesis.The observed z-statistic is –3. The chance of getting a sample average 3 SEs or more below its expected value is about 1 in 1,000.This is an observed significance level, denoted by P, and referred to as a P-value.
38Arsenic Example 4. Calculate the test statistic. 5. Z value is < 1.96(At 5% level of significance)So decisionEvidence are failed to reject the null hypothesisHence, alternate hypothesis is true
39Arsenic Example 7. State the conclusion. There is not significant statistical evidence that the average arsenic level exceeds 30 nanograms per liter at the 0.05 level of significance.
40Types of ErrorType I Error: When we reject H0 (accept Ha) when in fact H0 is true.Type II Error: When we accept H0 (fail to reject H0) when in fact Ha is true.Truth about thepopulationHo is trueHa is trueDecision basedon the sampleReject HoType IerrorCorrectdecisionAccept HoType II
41Power and Error Significance and Type I Error The significance level α of any fixed level test is the probability of a Type I Error: That is, α is the probability that the test will reject the null hypothesis, H0, when H0 is in fact true.Power and Type II ErrorThe power of a fixed level test against a particular alternative is 1 minus the probability of a Type II Error for that alternative (1 - β).
42PowerPower: The probability that a fixed level α significance test will reject H0 when a particular alternative value of the parameter is true is called the power of the test to detect that alternative.In other words, power is the probability that the test will reject H0 when the alternative is true (when the null really should be rejected.)Ways To Increase Power:Increase αIncrease the sample size – this is what you will typically want to doDecrease σ
43Significance LevelThe observed significance level is the chance of getting a test statistic as extreme as, or more extreme than, the observed one.It is computed on the basis that the null hypothesis is right.The smaller it is, the stronger the evidence against the null.
44The common practice of testing hypotheses The common practice of testing hypotheses mixes the reasoning of significance tests and decision rules as follows:State H0 and HaThink of the problem as a decision problem, so that the probabilities of Type I and Type II errors are relevant.Because of Step 1, Type I errors are more serious. So choose an α (significance level) and consider only tests with probability of Type I error no greater than α.Among these tests, select one that makes the probability of a Type II error as small as possible (that is, power as large as possible.) If this probability is too large, you will have to take a larger sample to reduce the chance of an error.
45The t-testThe z-test is good when the sample size (number of draws) is large enough.For small samples, the t-test should be used, provided the histogram of the contents is not too different from the normal curve.
46The t Distribution: t-Test Groups and Pairs Used often for experimental datat-test used when:Sample size is small (e.g.: < 30)Dependent variable measured at ratio levelRandom assignment to treatment/control groupsTreatment has two levels onlySample statistic normally distributed
47t difference between the means = standard error of the difference The t-test represents the ratio between the difference in means between two groups and the standard error of the difference. Thus:difference between the meansstandard error of the differencet=
48Two t-Tests: Between- and Within-Subject Design Between-subjects:Used in an experimental design, with an experimental and a control group, where the groups have been independently establishedWithin-subjects:In these designs the same person is subjected to different treatments and a comparison is made between the two treatments.
49Example A weight is known to weigh 70 g. Five measurements were made: 78, 83, 68, 72, 88. The fluctuation is due to chance variation assuming that the calibrated instrument was used for measurement.Do the measurements fluctuate around 70, or do they indicate some bias in the measurement procedure?
50Summary for the t-test Assumptions: (a) The data are likely drawn from a normally distributed population.(b) The SD of the population is unknown.(c) The number of observations is small.(d) The histogram of the contents does not look too different from the normal curve.
51Two Sample TestsTo compare two samples averages, the test statistic has the formexcept that now these terms apply to difference of sample averages.Need to find the SE for difference.
52Summary for the t-testThe t-statistic has the same form as the z-statistic:except that the SE is computed on the basis of SD of the data.The degree of freedom is (sample size – 1).
53Used for frequencies in discrete categories. The Chi Square TestUsed for frequencies in discrete categories.(a) To check if the observed frequencies are like hypothesized frequencies.(b) To check if two variables are independent in the population.
54Expected frequency for a cell = (row total X column total)/NThe χ2 statistic is:where the sum is taken over all the cell.
55Is the die loaded?A gambler is accused of using a loaded die, but he pleads innocent. The results of the last 60 throws are summarised below:Value observed frequency
56In our example, the χ2-statistic is Large χ2 valuesobserved frequency is far from expected frequency: evidence against the null hypothesis.Small χ2 valuesthey are close to each other: evidence for the null hypothesis.
57What’s the P-value,i.e., under the null hypothesis, what’s the chance of getting a χ2-statistic that is as large as, or larger than the observed statistic?Find out df, Chi square value and find out p
58Conclusion A test of significance answers a specific question: How easy is it to explain the difference between the data and what is expected on the null hypothesis, on the basis of chance variation alone?It does not measure the size of a difference or its importance,It will not identify the cause of the difference.
59Data type 2. Distribution of data 3. Analysis type (goal) Choosing a stat test……Data type 2. Distribution of data 3. Analysis type (goal)4. No. of groups 5. Design