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No, often there is not enough evidence either way.
It really depends on what your hypothesis is! But I expect a hypotenuse could be more useful in this context. However, knowledge of the hypothesis, hypotenuse even, is not enough to enable you to find the perimeter.
The statement formed when you negate the hypothesis and conclusion of a conditional statement. For Example: If you had enough sleep, then you did well on the test. The inverse will be: If you didn't have enough sleep, then you didn't do well on the test.
You can test a hypothesis with very little information. For hypothesis testing you will have a null hypothesis, and alternative and some test statistic. The hypothesis test consists of checking whether or not the test statistic lies in the critical region. If it does, then you reject the null hypothesis and accept the alternative. The default option is to stick with the null hypothesis.If the number of observations is very small then the critical region is so small that you have virtually no chance of rejecting the null: you will default to accepting it.Different test have different powers and these depend on the underlying distribution of the variable being tested as well as the sample size.
That means that you should roll a die many times, count how often you get the number "2", then divide this by the total number of rolls. If the die is "fair" (no extra weight on one side), you would expect this experimental probability to be somewhere close to the theoretical probability of 1/6, at least, if you roll often enough.