There are generally two types of hypotheses, the null and the alternative, null is denoted as H nought or some people say H subzero, the alternative is denoted as H sub a, I just say H a, the null hypothesis is a basic statement about our topic, this statement generally assumes that nothing has changed, for us, that would mean that the average height for women is equal to 64 inches, that's exactly what our null would be, we would always start by assuming that the null hypothesis is true, the alternative hypothesis is always the opposite of the null, so in this case, it would be that the mean height for women is not 64 inches, if the null hypothesis isn't directional, the alternative hypothesis shouldn't be directional, for example, if the historical data showed that women have always been shorter than or equal to 64 inches, then that would be the null, the alternative in that case would be that the average height is greater than 64 inches. To test whether the average height has changed, since the last measurement, we need to first construct a range around that 64, we construct this range, so that if the height has not changed, that being if the mean is still 64, it would be very likely that the mean of our sample would fall inside this range, on the other hand, if the mean of our sample has changed and falls outside of our range, we can feel confident in saying that the average height is probably different now, than it was in the past, if the sample mean falls within the range, then we don't have sufficient evidence to support the claim that the height has changed. I want to go back to our example of women's heights, you've learned that the historical average height is 64 inches, so let's base our hypotheses off this historical parameter.
When you run your experiments, you'll have to formulate hypotheses and test them out, there's a lot involved in doing that, but let's cover the basics.
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