--- title: "Hypothesis Testing- Discovery" subtitle: "Notes and in-class exercises" format: html: embed-resources: true toc: true --- You can download the .qmd file for this activity [here](../activity_templates/22-hypothesis-testing-discovery.qmd) and open in R-studio. The rendered version is posted in the [course website](https://mutasim221b.github.io/Mac-STAT-155-Fall-25/) (Activities tab). I often experiment with the class activities (and see it in live!) and make updates, but I always post the final version before class starts. To be sure you have the most up-to-date copy, please download it once you’ve settled in before class begins. ```{r setup} #| include: false knitr::opts_chunk$set( collapse = TRUE, warning = FALSE, message = FALSE, error = TRUE, fig.height = 2.75, fig.width = 4.25, fig.env = 'figure', fig.pos = 'h', fig.align = 'center') ``` # Notes - You can download a template file to work with [here](../activity_templates/22_hypothesis_testing_discovery.qmd). - **File organization:** Save this file in the "Activities" subfolder of your "STAT155" folder. ## Learning goals By the end of this lesson, you should be able to: - Understand how standard errors and confidence intervals enable us to make statistical inferences - Articulate how we can formalize a research question as a testable, statistical hypothesis ## Readings and videos This is a discovery activity, so no assigned readings/videos today. # Exercises Let's return to the `fish` dataset. Recall that rivers contain small concentrations of mercury which can accumulate in fish. Scientists studied this phenomenon among 171 largemouth bass in the Wacamaw and Lumber rivers of North Carolina, recording the following: | variable | meaning | |:---------|:---------------------------------------------------------| | River | Lumber or Wacamaw | | Station | Station number where the fish was caught (0, 1, ..., 15) | | Length | Fish's length (in centimeters) | | Weight | Fish's weight (in grams) | | Concen | Fish's mercury concentration (in parts per million; ppm) | ```{r} # Load the data & packages library(tidyverse) fish <- read_csv("https://mac-stat.github.io/data/Mercury.csv") head(fish) ``` ## Exercise 1 **Research question:** Is there evidence that the mercury concentration in fish (`Concen`) differs according to the `River` they were sampled from? ### part a: fit the model Fit a simple linear regression model that would address our research question ```{r} mod_fish <- lm(Concen ~ River, data=fish) summary(mod_fish) ``` Interpret the intercept from this model. > Our model estimates an average mercury concentration of 1.078ppm among fish in the Lumber River. ### part b: construct a CI Using the 68-95-99.7 rule, construct an approximate 95% confidence interval for the intercept term, and provide an appropriate interpretation. ```{r} # approximate 95% confidence interval ``` > **Response** Compare your CI to an exact 95% confidence interval for the model coefficients: ```{r} confint(mod_fish, level=0.95) ``` ### part c: what can we conclude from multiple samples? Suppose we take 200 different samples of fish from the Lumber River. Based on these results, in how many of those samples would you expect to observe mean mercury concentration **greater than** 1.25ppm? > **Response** ### part d: intuition for constructing & interpreting test statistics Suppose previous environmental studies have found little evidence of mercury pollution in other rivers in the area, so perhaps our "default" assumption is that fish from the Lumber river *should* have an expected mercury concentration of 0ppm. How many standard errors is our sample estimate (1.078ppm) away from this expectation? What are three possible conclusions? > **Response** ### part e: do individual observations contradict our conclusions? Now suppose we sample a *single* fish from the Lumber River and find it has a mercury concentration of 2.5ppm. Are you surprised by this result? Why or why not? (Hint: create a code chunk that calculates the mean, standard deviation, and maximum of the `Concen` variable in each river in our original sample) ```{r} fish %>% group_by(River) %>% summarise(mean=mean(Concen), sd=sd(Concen), max=max(Concen)) ``` > **Response** > Class Notes ## Exercise 2: Finish after the class Let's look at the model summary output again: ```{r} summary(mod_fish) ``` ### part a: interpret model coefficient Now, let's interpret the `RiverWacamaw` coefficient. Based *only* on the coefficient (don't think about the standard error yet), what can we say about the difference in mercury concentration among fish in the two rivers? > **Response** ### part b: construct a CI Using the 68-95-99.7 rule, construct an approximate 95% confidence interval for the `RiverWacamaw` coefficient, and provide an appropriate interpretation. > **Response** ### part c: interpreting the CI Do you believe it *plausible* that the mean mercury concentration of the fish population in the Wacamaw River is approximately the same as that of the fish population in the Lumber River? How would you confirm this? What assumptions are you making? > **Response** ### part d: effect of sample size on our conclusions Suppose we sample 10 times as many fish from the Wacamaw River, and get a similar coefficient estimate (0.2). Thinking back to the Central Limit Theorem, what should happen to the standard error of the `RiverWacamaw` coefficient? How small of a standard error would we need to more conclusively say that there is an actual difference in mean mercury concentrations of the Lumber River and Wacamaw River fish populations? > **Response** ### part e: reconciling parameter estimates and uncertainty Suppose the *true* population coefficient for the `RiverWacamaw`parameter is 0.02 (i.e. the average mercury concentration is 0.02ppm higher for the Wacamaw River fish population compared to that of the Lumber River). Is this meaningful? > **Response** ### part f (CHALLENGE) Using the model summary output, report the mean mercury concentration for our sample of fish from the *Wacamaw River*: ```{r} summary(mod_fish) ``` > **Response:** Which of the following values do you think is the standard error of the sample mean for the Wacamaw River? - 0.11712 - 0.08866 - 0.11712 + 0.08866 = 0.20578 - 0.11712 - 0.08866 = 0.02846 - something else To answer this question, look at the code chunk below, which fits the same model, but uses the Wacamaw River as our reference category instead of the Lumber River: ```{r} mod_fish2 <- lm(Concen ~ River, data=fish %>% mutate(River=ifelse(River == "Wacamaw", paste0("_", River), River))) summary(mod_fish2) ``` Compare this to the output for `mod_fish`. What do you notice about the standard errors of the intercepts (i.e., the standard errors of the means for each river) compared to the standard errors of the `RiverWacamaw` and `RiverLumber` coefficients (i.e., the standard errors of the **differences** between the means)? > **Response:** \ \ \ \ # Solutions ```{r eval = TRUE, echo = FALSE} # Load the data & packages library(tidyverse) fish <- read_csv("https://mac-stat.github.io/data/Mercury.csv") ``` ## Exercise 1 **Research question:** Is there evidence that the mercury concentration in fish (`Concen`) differs according to the `River` they were sampled from? ### part a: fit the model Fit a simple linear regression model that would address our research question ```{r eval = TRUE} mod_fish <- lm(Concen ~ River, data=fish) summary(mod_fish) ``` Interpret the intercept from this model. > Our model estimates an average mercury concentration of 1.078ppm among fish in the Lumber River. ### part b: construct a CI Using the 68-95-99.7 rule, construct an approximate 95% confidence interval for the intercept term, and provide an appropriate interpretation. > 1.078 +/- 2*0.089 --> [0.90, 1.256] > Preferred interpretation: It is plausible that the true mean mercury concentration among fish in the Lumber River is between 0.90ppm and 1.25ppm. > (technical addendum to this interpretation): ...specifically, we expect that if we take many different samples and obtain a set of corresponding parameter estimates and confidence intervals, we expect that 95% of the resulting intervals will contain the true mean mercury concentration of the entire Lumber River fish population. We hope that our interval is one of the lucky 95% and not one of the unlucky 5% that don't contain the true population parameter. > Not as preferred interpretation: We are 95% confident that the mean mercury concentration among fish in the Lumber River is between 0.90ppm and 1.25ppm. Compare your CI to an exact 95% confidence interval for the model coefficients: ```{r eval = TRUE} confint(mod_fish, level=0.95) ``` ### part c: what can we conclude from multiple samples? Suppose we take 200 different samples of fish from the Lumber River. In how many of those samples would you expect to observe an estimated mean mercury concentration **greater than** 1.25ppm? > We don't/can't actually know! This depends on the true population parameter and the accuracy of our sampling distribution. > What we *can* say is that if our sampling distribution model is accurate, then we should expect that about 10 out of 200 samples (5% of them) will produce confidence intervals that *don't* contain the population parameter. We should expect that half of these--so 5 samples--are overestimates and the other half are underestimates. ### part d: intuition for constructing & interpreting test statistics Suppose previous environmental studies have found little evidence of mercury pollution in other rivers in the area, so perhaps our "default" assumption is that fish from the Lumber river *should* have an expected mercury concentration of 0ppm. How many standard errors is our sample estimate (1.078ppm) away from this expectation? What are three possible conclusions? > If we assume that 0ppm is the "true" mercury concentration, then our estimate of Beta_0 = 1.078ppm with a standard error of 0.08866 means that our estimate is (1.07808-0)/0.08866 = 12.16 standard errors away from what we should expect. > Possible conclusions: > 1) Our working assumption that 0ppm should be the "true" mercury concentration in the Lumber river fish population was wrong! The confidence interval we constructed above suggests that a true value of 0ppm is extremely implausible. > 2) Perhaps ~0ppm is actually the true average mercury concentration in the population, we just got extremely, outrageously unlucky with our sample. > 3) Perhaps there was a measurement/data entry error, and the units are actually parts per billion, not million. ### part e: do individual observations contradict our conclusions? Now suppose we sample a *single* fish from the Lumber River and find it has a mercury concentration of 2.5ppm. Are you surprised by this result? Why or why not? (Hint: create a code chunk that calculates the mean, standard deviation, and maximum of the `Concen` variable in each river in our original sample) ```{r eval = TRUE} fish %>% group_by(River) %>% summarise(mean=mean(Concen), sd=sd(Concen), max=max(Concen)) ``` > Observing a *single* fish with a mercury concentration of 2.5ppm is actually not that surprising! 2.5ppm is a little more than 2 standard deviations away from the mean mercury concentration in our sample of fish from the Lumber River (1.08+2*0.64=2.43), but there are certainly fish in the sample with even higher mercury concentrations (max=3.5ppm), so this isn't outside the bounds of what we'd expect. ## Exercise 2 Let's look at the model summary output again: ```{r eval = TRUE} summary(mod_fish) ``` ### part a: interpret model coefficient Now, let's interpret the `RiverWacamaw` coefficient. Based *only* on the coefficient (don't think about the standard error yet), what can we say about the difference in mercury concentration among fish in the two rivers? > The `RiverWacamaw` coefficient is 0.19835, meaning that the mean mercury concentration among fish in the Wacamaw River is, on average, about 0.20ppm *higher* than that of fish in the Lumber River. ### part b: construct a CI Using the 68-95-99.7 rule, construct an approximate 95% confidence interval for the `RiverWacamaw` coefficient, and provide an appropriate interpretation. > 0.20 +/- 2*0.11 --> [-0.02, 0.42] > Preferred interpretation: It is plausible that the true *difference* in mean mercury concentration among fish in the Wacamaw River compared to the Lumber River is between -0.02 ppm and 0.42ppm. > Not as preferred interpretation: We are 95% confident that the mean mercury concentration among fish in the Wacamaw River somewhere between 0.02ppm *less* than that of fish in the Lumber River and 0.42ppm *more* than that of fish in the Lumber River. ### part c: interpreting the CI Do you believe it *plausible* that the mean mercury concentration of the fish population in the Wacamaw River is approximately the same as that of the fish population in the Lumber River? How would you confirm this? What assumptions are you making? > Answers may vary--this is certainly plausible, since our 95% CI contains 0 (i.e., there is no difference in means between the two rivers). However, we might also argue that there is SOME evidence of a difference, since most of the CI is > 0. ### part d: effect of sample size on our conclusions Suppose we sample 10 times as many fish from the Wacamaw River, and get a similar coefficient estimate (0.2). Thinking back to the Central Limit Theorem, what should happen to the standard error of the `RiverWacamaw` coefficient? How small of a standard error would we need to more conclusively say that there is an actual difference in mean mercury concentrations of the Lumber River and Wacamaw River fish populations? > A larger sample should result in a smaller standard error of the `RiverWacamaw` coefficient. If the standard error is smaller than 0.1 (say 0.098), then a 95% confidence interval would be [0.004, 0.396]. Since this interval doesn't include 0, we could conclude that fish in the Wacamaw River, on average, have a higher mercury concentration than fish in the Lumber River. More importantly, the lower standard error of the coefficient allows us to say there is evidence *that this difference should be observable across new samples.* ### part e: reconciling parameter estimates and uncertainty Suppose the *true* population coefficient for the `RiverWacamaw`parameter is 0.02 (i.e. the average mercury concentration is 0.02ppm higher for the Wacamaw River fish population compared to that of the Lumber River). Is this meaningful? > This will depend on context--a priori, this difference appears to be negligible, and we could potentially chalk it up to uncontrolled confounders (e.g., perhaps fish in one river tend to be older/bigger and therefore have slightly higher mercury concentrations, even if there is no underlying difference in mercury pollution). We also might consider: what is considered a "harmful" mercury concentration, and are fish in either river near that threshold? Has this changed over time, and by how much? ### part f: (CHALLENGE) Using the model summary output, report the mean mercury concentration for our sample of fish from the *Wacamaw River*: ```{r eval = TRUE} summary(mod_fish) ``` > 1.07808 + 0.19835 = 1.27643ppm Which of the following values do you think is the standard error of the sample mean for the Wacamaw River? To answer this question, look at the code chunk below, which fits the same model, but uses the Wacamaw River as our reference category instead of the Lumber River: ```{r eval = TRUE} mod_fish2 <- lm(Concen ~ River, data=fish %>% mutate(River=ifelse(River == "Wacamaw", paste0("_", River), River))) summary(mod_fish2) ``` Compare this to the output for `mod_fish`. What do you notice about the standard errors of the intercepts (i.e., the standard errors of the means for each river) compared to the standard errors of the `RiverWacamaw` and `RiverLumber` coefficients (i.e., the standard errors of the **differences** between the means)? > SEs for the means are 0.08866 and 0.07652 for the Lumber and Wacamaw rivers, respectively. The SE for the difference is the same in both models (0.11712), which is greater than the SE of either mean. Because standard errors quantify uncertainty in a given parameter estimate, this tells us that the uncertainty of the estimated difference between two means is greater than the uncertainty in our estimate of either mean by itself.