 तो चुके ये पेरामेट्रिक टेस्त हैं, और पेरामेट्रिक टेस्त के अंदर हमेशा पेरामेट्रिक में, we make some assumptions about the underlying population. और उसी को हम अस्टिमेट करना चारे होतने हैं, और हमारी जम्षन है के जो अंदर्लाइंग पापूलेट्चन है, और नोमली दिस्टिबुटिट है. और पेरामेट्रिक में में में और शांपार लगाट तेने है, दो हमारी जम्षन है। ती तीरीस के आंदर, सामपल साऽिस बडी बहो इनहाग़ था है, और सामपल साऽसक का अंद्खलय है, और और अंदर्द शाम्पल साऽीझ एक ठान्टीड कन्दरिए लेग, सम्ने अभी बात की के वी अप्रोच तो नोमल दिस्टिबूशन हमारी ती स्टिटिस्टिस्टिक की वालु बड़ी होती जाती जाती है, and that approaches significance. The number of scores in the sample and the magnitude of the sample variance both have large effect on the statistic and thereby influence the statistical decision. So not only the sample size, but the sample standard deviation or the sample variance or variability also influences the statistic and effect. If our sample variance, that is the standard deviation of the sample, that is the variability in the data, if it is increasing or it is large or the value of standard deviation is large, then what will happen, because you know that variance comes in our denominator, in T, so when we calculate it in T, so as our standard deviation increases, the value of the denominator will increase, and our T value will become small. And if the value of T is small, then it cannot approach significance. The estimated standard error is directly related to the sample variance, so that the larger the variance, the larger the error. As our standard deviation value increases, our standard error will also increase, and our standard error will also increase. Standard error means that we approximate it and the difference between the sample and the population of the statistic and the parameter. Not only the standard deviation will increase, but the standard deviation will also increase. Similarly, the influence of the sample size will also increase, so that the estimated standard error is inversely related to the number of scores in the sample. The larger the sample, the greater the value of the standard error will increase. So, if our standard error increases, then our standard error will also increase. So, the estimated standard error is inversely related to the number of scores in the sample, larger the sample, smaller the error. You can just take a piece of paper and just play with the data. You can just randomly plug in the data that if we have S value 2 and N value 4 and if we have S value 6 and if we have N value 4, then what will be our T value? Similarly, you can practice and play with the data a little bit. If we have S value 2 and if we have N value 10, then what will be the effect on the T value? So, you can play with the data bit and you can find how the amount or size of the standard deviation affects the size of standard error and how standard error affects the T value. Similarly, how sample size affects the T value.