 हूँ आप भी ज़ा।, मेंंवलीं नहीं टी स्वटतीक्स, अप भी नहीं आप लिए कलगे राई। तो वो त्येज़ और द्योड़्चा क्यचा दे आप ज़े करी है। थो है नहीं लेगा और लगा पार्ञ्टी आप फ्रूप रीवान बीगा लिए तूफ फरीवाद बीगा दभीगा नहीं मेंN खेलाग, थो रहां बीगा और लेगा, बीगा दीखा भुतकि थाने हैंसे खुल्प फ्रूथा, वर भी बासकती क्स मेंन तुए वादी पाप्लट तीट्मन्त is administered to the individual in the sample after treatment, sample has mean of 44 like some populations come in 40 group treatment, the treatment to other mean 44 and variance or any S square is 16 so now we know the sample variance we know the population variance यो ता उब कासे ख़ेगों दूप की ख़ो ने, उस्किलोग मोग की लिए आखोग तो बना चिलोग, नहीं र पर सेटिटंटी्ग, लेई, रारे तो सो पक्वाग, मेंव वखे लिए, वशां चा प्रिएः, छाचाद,लिए, अब कोक सेदिटंटी दंदी, पर सेटिट ये व सकौयर गब ज़ोसा, इसे स्वानदर दवीशन की वेलूम निकालते है, और हमने की आत चूके एन भीश अस्टीमेशन, अमने उसको ऐसे कर लिए ता एन मानवस स्वान कर गब. उब हमारे पास उनो ने कुश्छन केंधर, स्टान्डद इवायश्चंटल की वेलिए होगमे, दीवी याविछ़्द अज़ स्च्टीन, तो हमें उनोने वेरिवियईज़ की वायविए वायविए स्च्टीन की वायविए भी एवायविए, अम इसंक यizon लएखगे डीव잖हेगे पहले हम स्तानथ एररंया जालेगे साँध साँब छए मरत करते खाल्वावा shah communication अम ईस आद छो़ाह [? भ spaces, Erin's- Meine Girian mover. साँभ करने वो आसनाता, न्सा मिसद carefully औरा सlearning बरों थी, अख और लतान। तो आप आईग decirन्गा hotelistic high hypothesis आआ सलोग न wording खीणी स् siinä थारी वीक से क्रतिकर लुग according to the तो हम ने क्याल्कूलेट की है तो ती, it is falling in the acceptance region और आप को याद है के this is acceptance region for null hypothesis तो उसका मतलब है, के we fail to reject the null hypothesis क्यो, कि हमारी क्याल्कूलेट वालू जो है, वो smaller है than the table value तो उसका मतलब है, के we fail to reject null hypothesis तिसका मतलब है, कि हां, मूँ is equal to 40 जिसका मतलब है, के उस त्रीट में का, कुई is no significant effect of that treatment यही है, even if it is 44 बच्तिल, we will conclude के based on this sample and based on the sample of 4 तो ती, this is not a significant to conclude के त्रीट में का significant effect है तो लेज्ट तो, another example with the one-tail test उजली ती, statistics के अंगर, हम खुट से ही डफाएन करते है, based on our hypothesis कि हम ने one-tail test करना है, यह two-tail test करना है त्रीट में के अगर, मुझे पीषे प्राए ड़ेटा यह मुझे यस त्रीट करने है, आई कुए त्रीट में पन्जाब इनुवस्टी में और में केती हुं, के एवरज आई कुए वो 100 से जादा है तो उसका मगगे आई में के इन अजम्षन अन दब पोजटीट ड़ेल कि पापूलेट्टिन का मीन, इस पापूलेट्टिन कि अनदा इस गरेटा द़े नहन्रद, यह और आई वल गो पे वे वो वी वो राई तेल तेस्टिं, इसी तरा अगर मुझे एक वो पे लग़ा है, यह तो आगर तेल पापुलेट्टिन के आगर नहीं तो पूलेट्टिन के पापूलेट्टिन सी च़ाद नहीं यह ख़ाद से अची बईदागे देटा, लिई बापुड़षे वो लिए आई, लिए वया, अपने पिछले एक्छाम्पल में पाँमलेट किया, के पापूलोशन कर में इस नात एकवल तो फोड, यह तो समहो लिसर और ग्रेटर दन दाड. तो लेच तो लिए एक्छाम्पल with the one tale, in a study, the research question is whether attractiveness affects the behavior of infants looking at photographs of women's faces. The researcher predicts that the infants will spend more time, more than half of the 22nd period looking at the attractive face. The researcher tested the sample of N9 and infants and obtained the mean of 13 seconds looking at the attractive faces with the variance of 72. So, in this example, what information is given to us. The assumption is that the infants will spend more than half of the 22nd period looking at the attractive faces. So, we will do a plug-in in the example. First step is to form our null hypothesis. Null hypothesis is equal to the mu that is smaller than 10. Less than 10 is equal to the mu that is smaller than 10. Less than half of the 22nd, infants will focus on the attractive face. But actually, because the 13 seconds have come in the data, we will say that the mu is greater than 10 seconds. So, this is our alternative hypothesis. We know the formula for T, mean minus mu divided by S over N under root. So, what will we do first? We will calculate S over N under root. So, we know that we have to calculate S which is equal to SS over N minus 1, which is unbiased estimation. SS was given to us by 72. And after that we have N, 9 minus 1 is 8. So, 8 divided by 72 would be equal to 9. So, we have S square, we have SS over N, we will remove variance. We have sum of squared value dv, which is 72. And then we will do N minus 1, which is equal to 8. 8 divided by 72 would be equal to 9. So, what will we do now? We will calculate the standard error value, which is variance divided by N under root. We have 9 variance. We have 9 variance. And N value is also 9. So, 9 divided by 9 is equal to 1. So, we have the standard error value. Now, we will plug in the value of T. We have the mean of the sample. We have taken out 13 seconds. You have seen the example. And for the population, we have taken half of the 20 seconds, which is equal to 10. And we have calculated the standard error. We will make it 1. So, 3 divided by 1, which is equal to 3. So, T's value is now 3. So, T calculated is 3. And we have to compare it with the table value. So, now we will see the table value. T's table value at degrees of freedom is 9 minus 1 over 8. Now, you can also see the table value. 8 over alpha 0.05 over 1 tail, our value is 2.89 divided by T's critical table. So, 3 actually is greater than 2.89. So, whenever our value is greater, then where will it fall? This is our critical region 1 tail, which is equal to 2.89. And 3 will fall here. So, this is our acceptance. This is our rejection region. This means that 3's value is calculated by table value, which is falling in rejection region. So, we will reject the null hypothesis. So, null hypothesis is rejected. So, testing hypothesis using T test, testing the claim that babies or infants are looking at our tract of faces, average time, how much is it? We will reject the null hypothesis and our alternative hypothesis will be accepted, which means that mu is greater than 10. And we will say that it is not half of the 22nd bulk. It is greater than 10 seconds. So, this is how we calculate manually. But in SPSS, it is just a one-click game. But you should know that what are the assumptions of T. How did we do mean or mu? And how did we know the standard variance and sigma of the population. So, we put in sample value. We calculated the T. We compared the calculated T with the table T. And then we made our decision about our hypothesis. Either we will reject or we will fail to reject.