No hypothesis test is 100% certain. Because the test is based on probabilities, there is always a chance of making an incorrect conclusion. When you do a hypothesis test, two types of errors are possible: type I and type II. The risks of these two errors are inversely related and determined by the level of significance and the power for the test. Therefore, you should determine which error has more severe consequences for your situation before you define their risks.Type I errorWhen the null hypothesis is true and you reject it, you make a type I error. The probability of making a type I error is α, which is the level of significance you set for your hypothesis test. An α of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. To lower this risk, you must use a lower value for α. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists.Type II errorWhen the null hypothesis is false and you fail to reject it, you make a type II error. The probability of making a type II error is β, which depends on the power of the test. You can decrease your risk of committing a type II error by ensuring your test has enough power. You can do this by ensuring your sample size is large enough to detect a practical difference when one truly exists.
Answer: Most People of India (75%) are of Aryan descent and live in the northern part of India. Nearly about 22% of Indians are of Dravidian origin and live in the south
There is strong belief among the Hindu majority that harm cannot come to such a sacred river.
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Answer: One economic factor leading to gender inequality in agriculture is the division of jobs, because there's an intrinsical credence that men are simply better equipped to handle certain jobs. Most of the time, those are the jobs that pay the best. This is inequitable because the results are lower income for women.
