Statistics and probability
- Writing hypotheses for a significance test about a mean
- Writing hypotheses for a test about a mean
- Conditions for a t test about a mean
- Reference: Conditions for inference on a mean
- Conditions for a t test about a mean
- When to use z or t statistics in significance tests
- Example calculating t statistic for a test about a mean
- Calculating the test statistic in a t test for a mean
- Using TI calculator for P-value from t statistic
- Using a table to estimate P-value from t statistic
- Calculating the P-value in a t test for a mean
- Comparing P-value from t statistic to significance level
- Making conclusions in a t test for a mean
- Free response example: Significance test for a mean
Writing hypotheses for a significance test about a mean
Example of constructing hypotheses for a test about a mean.
Want to join the conversation?
- Why do we include the probability of getting values farther away than our sample mean and not just the value on the dot? (2:01)(6 votes)
- because they want to find out if it differs from 500,L at all, in either direction, not just in one direction.(1 vote)
- Why would the "no news here" be mu=500? to me it seems it could be either way. Can you please supply more concrete and less subjective methodology for determining the null hypothesis?(1 vote)
- why do you include (2:01) value the dot?(0 votes)
- [Instructor] A quality control expert at a drink bottling factory took a random sample of bottles from a batch and measured the amount of liquid in each bottle in the sample. The amounts in the sample had a mean of 503 milliliters and a standard deviation of five milliliters. They want to test if this is convincing evidence that the mean amount for bottles in this batch is different than the target value of 500 milliliters. Let mu be the mean amount of liquid in each bottle in the batch. Write an appropriate set of hypotheses for their significance test, for the significance test that the quality control expert is running. So pause this video and see if you can do that. Now, let's do this together. So first, you're going to have two hypotheses. You're gonna have your null hypothesis and your alternative hypothesis. Your null hypothesis is going to be a hypothesis about the population parameter that you care about and it's going to assume kind of the status quo. No news here. And so the parameter that we care about is the mean amount of liquid in the bottles in the batch. So that's mu right over there. And what would be the assumption that that would be, the no news here? Well, it would be 500 milliliters. That's the target value. So, it's reasonable to say, well, the null is doing what it's supposed to, that where the actual mean for the batch is actually what the target needs to be, it's actually 500 milliliters. Some of you might have said, hey wait, didn't they say the amounts in the sample had a mean of 503 milliliters? Why isn't this 503? Remember, your hypothesis is going to be about the population parameter. Your assumption about the population parameter. This 503 milliliters right over here, this is a sample statistic. This is a sample mean that's trying to estimate this thing right over here. When we do our significance test, we're going to incorporate this 503 milliliters. We're going to think about, well, what's the probability of getting a sample statistic, a sample mean this far or further away from the assumed mean if we assume that the null hypothesis is true, and if that probability is below a threshold, our significance level, then we reject the null hypothesis and it would suggest the alternative. But if we're just trying to generate or write a set of hypotheses, this would be our null hypothesis, and then our alternative hypothesis is that the true mean for the batch is something different than 500 milliliters.