The confidence interval depends on a variety of parameters,. Introduction. One of the proposed confidence intervals is based on the normal approximation. 5, lies within either of the 95-percent confidence intervals. A confidence interval is a interval of plausible values for a population characteristic (e. The steps in constructing this confidence interval are given below. Another way to say this is that is the best point estimate of the true value of μ. Let's view the first 6 intervals. Tutorial for new R users whom need an accessible and easy-to-understand resource on how to create their own histogram with basic R. A histogram is a plot with rectangles, height of which represents the frequency or "count" of the occurrence and width is equal to the grouping interval. In DescTools: Tools for Descriptive Statistics. Caution: This procedure requires a planning estimate of the sample correlation. Method π N μ σ a b Click here for interval formulas for proportions. Summary of Video If we know an entire population, then we can compute population parameters such as the population mean or standard deviation. When plotted as bar charts, the underlying true den-sity information may be quite distorted. Following Agresti and Coull, the Wilson interval is to be preferred and so is the default. groupwiseMean function for grouped and ungrouped data. The approximation, however, might not be very good. Confidence Interval for a Proportion (Activity 8) Learn how to use JMP to construct a confidence interval for a proportion. Using Stata for Confidence Intervals All of the confidence interval problems we have discussed so far can be solved in Stata via either (a) statistical calculator functions, where you provide Stata with the necessary summary statistics for means, standard deviations, and sample sizes; these commands end with an i, where the i. The total area of a histogram used for probability density is always normalized to 1. Suppose we want to estimate and obtain the confidence interval estimate of the average GPA ($\mu$) of all undergraduate students at University of Notre Dame. Confidence intervals. and Keeping, E. Plot Group Means and Confidence Intervals - R Base Graphs we’ll describe how to create mean plots with confidence intervals in R. I used optim command to obtain the maximum likelihood estimates using some starting values. The definition of histogram differs by source (with country-specific biases). For numeric variables, we can summarize data with the center and spread. Looking for elementary statistics help?You've come to the right place. 01 level (two-tailed). Plot a histogram of your bootstrap replicates. The Sampling Distribution of the Mean Confidence Intervals for a Proportion Goal: To gain experience with the sampling distribution of the mean, and with confidence intervals for a proportion. Assumptions and Conditions \n. confidence interval: Under Stat, select T statistics, then two samples. Chapter 3 R Bootstrap Examples Bret Larget February 19, 2014 Abstract This document shows examples of how to use R to construct bootstrap con dence intervals to accompany Chapter 3 of the Lock 5 textbook. Be able to explain the bootstrap principle. Bootstrapping in R – A Tutorial confidence intervals of their respective R- •View histograms (frequency and density). Produce a histogram of residuals and a plot of residuals vs. Click OK to create the table. 7 shows how to compute confidence intervals for the unknown population mean \(E(Y)\). Asymptotically, we know that thus, an approximation for the variance matrix of will be based on , obtained by plugging estimators. Compute Confidence Intervals. Call this probability π. 1 is small but not trivial. This spreadsheet calculates 90%, 95%, and 99% confidence intervals for correlations. Confidence Intervals for Scikit Learn Random Forests¶. As an example, I've created some fake data, by simulating 100 data points from a standard Normal distribution:. R Statistical Package. what is the command for that. 2 Asymmetric Confidence Interval for the Mean 76 3. In this case, we'll use the summarySE() function defined on that page, and also at the bottom of this page. Calculate the sample average, called the bootstrap estimate. Note that R changes the label for the confidence interval (90 percent …) to reflect the specified confidence level. As the sample mean increases, the width remains the same. Credibility Intervals 26/11/2014 Arthur Charpentier 10 Comments Tomorrow, for the final lecture of the Mathematical Statistics course, I will try to illustrate – using Monte Carlo simulations – the difference between classical statistics, and the Bayesien approach. Both gave a 95% confidence interval of 23. Confidence Intervals A confidence interval is ideal for quantifying the degree of uncertainty around common parameters of interest such as the center of a sampled population, or its spread. R has built in functions for a large number of summary statistics. This will find the 95% confidence interval for the variance of the population given the input data. The formulas are slightly different, but they follow the same reasoning. I try to plot a prediction interval and a Confidence interval, of a linear regression fit. A confidence interval in short CI is a type of interval estimate of a population parameter. How to calculate the confidence interval. 1 Summary Statistics. 95% Confidence Interval for Median 95% Confidence Interval for Sigma 95% Confidence Interval for Mu Anderson-Darling Normality Test Descriptive Statistics In this case we see that the mean and median are now very similar, and the boxplot shows the presence of no outliers. So, I obtained the betas and then the fitted values and the confidence intervals. A bootstrap interval might be helpful. R 's default with equi-spaced breaks (also the default) is to plot the counts in the cells defined by breaks. You can also make histograms by using ggplot2 , "a plotting system for R, based on the grammar of graphics" that was created by Hadley Wickham. Break points make (or break) your histogram. Therefore, I compute a ß. 5th percentiles of the sampling distribution. Confidence intervals are a bright yellow caution sign telling you to take that sample result with a grain of salt because you can’t be more specific than this range. Credibility Intervals 26/11/2014 Arthur Charpentier 10 Comments Tomorrow, for the final lecture of the Mathematical Statistics course, I will try to illustrate – using Monte Carlo simulations – the difference between classical statistics, and the Bayesien approach. For comparison, the 95% confidence interval for the arithmetic mean using the raw, untransformed data is 0. Lower bounds of these 100 confidence intervals are stored in lower, and the upper bounds are in upper. Presentation of Study Results: Histograms with Confidence Interval Summary Elke Dietrich Stefan GroS Heinz-Otto Keinecke Harald Kwasny - Behringwerke AG - Bebringwerke AG - Behringwerke AG - Bebringwerke AG The SAS® Macro CIRCLES (PC DOS, Release 6. The lower and upper confidence levels (CLs) show that the best results for normality were reached with Lambda values between -2. Note that this may change the exact length of the interval if the interval is shifted by a Period object. AU - Tofighi, Davood. Produce a histogram of residuals and a plot of residuals vs. ) The set A might or might not be an interval, depending on the nature of the test. The R codes above ask R to take the mean stored in samp, and compute the lower and upper bound of the confidence interval. Data Types: A) Numeric - numbers only, with or without a decimal point. data function. Confidence intervals are one method of interval estimation, and the most widely used in frequentist statistics. Substituting and in place of their true values, we can therefore calculate a 95% confidence interval for the difference in proportions as. The 95% confidence interval is presented as standard in research. A rule of thumb is to use a histogram when the data set consists of 100 values or more. The majority of intro-ductory statistics texts recommend tabulating data into intervals. Estimated probability of visitation as a function of leaf height with a correctly-computed 95% confidence interval superimposed. R Programming. At this point this is a reference for using R. • The confidence interval of PD can be derived from the approximation below. I have tried using t. This is about twice the number of bad confidence intervals that would be expected if the confidence intervals were proper 95% confidence intervals. For example, given a 99% confidence interval, stock XYZ's return will. # Bootstrap 95% CI for R-Squared. A Density Plot visualises the distribution of data over a continuous interval or time period. One of the proposed confidence intervals is based on the normal approximation. Plotting of the confidence interval is suppressed if ci is zero or negative. Why should I care about confidence intervals and significance? Confidence intervals provide a statistical measure for how reliably the sample data represents an underlying population. A confidence interval is the boundary between which the. 2% of the time in life. With an independent-samples t test, each case must have scores on two variables, the grouping (independent) variable and the test (dependent) variable. The user specifies N. Welcome to Applied Statistics with R! 1. 4 per cent - that is, 52 per cent plus or minus 4. Using the same R-function the Wald normal approximation, with small sample adjustment, gave a risk ratio of 11. RandomForestClassifier objects. Summary of Video If we know an entire population, then we can compute population parameters such as the population mean or standard deviation. Plot a histogram of your bootstrap replicates. Figure 5 shows the 95% percentile intervals for the interactions terms of interest. confidence interval: Under Stat, select T statistics, two samples, with data. This happens in particular with the standard packages we use for wildlife data analysis, where the output includes confidence intervals for each coefficient or real value. A 95% confidence interval is defined as an interval calculated in such a way that if a large number of samples were drawn from a population and the interval calculated for each of these samples, 95% of the intervals will contain the true population mean value. Be able to design and run an empirical bootstrap to compute conﬁdence intervals. I strongly advise to use a boxplot or a violin plot instead. If one starts with a two-tail z test or two-tail t test, one ends up with a confidence interval rather than a more general confidence set. Interval estimation of quantiles has been treated by many in the literature. It has expanded to include Cocoa, R, simple math and assorted topics. The following figure displays the histogram from 1000 sample means superimposed with a Gaussian curve. This rather depends upon the nature of your study. 17 with an interval 1. We will use it again in chapter 8 for hypothesis testing but for now it gives us the confidence interval for the mean. Confidence Intervals for Percentiles and Medians. Visualise the distribution of a single continuous variable by dividing the x axis into bins and counting the number of observations in each bin. ) The set A might or might not be an interval, depending on the nature of the test. The latter two depend on the dimensionless depth k p d. When constructing confidence intervals the assumptions and conditions of the central limit theorem must be met in order to use the normal model. A confidence interval is an interval estimate combined with a probability statement. At this point this is a reference for using R. Key output includes the histogram, the estimate of the mean, and the confidence interval. RandomForestClassifier objects. I plot these in a histogram and would like to include a confidence interval for the height of each histogram bar. Group) t1(Example of graph comparing 95% confidence intervals) yline(0) xlabel(, valuelabel) 5) The above commands yield the following plot: -5 0 5 10 15 20 25 30 35 Change M < 30 M 30+ F < 30 F 30+ Group 95% Confidence Interval. Two ways I usually make error bars: 1. I am worried about assertaining confidence intervals for a limited validation sample. A rule of thumb is to use a histogram when the data set consists of 100 values or more. Bootstrap Confidence Intervals. Let's understand this — we want to estimate the mean, μ, of the underlying population distribution and we have access to a random sample containing n observations with standard deviation s. However, I would expect much closer to 900 of the confidence intervals for a normal distrubution at a 90% confidence. The only notion of "theoretical confidence interval" that I know of is the one that I alluded to, where you know $\sigma$ but not $\mu$ in advance. I have got the fitted values and the confidence intervals as vectors. So at best, the confidence intervals from above are approximate. SCOTT Frequency tables are often constructed on intervals of irregu-lar width. See the section Shapiro-Wilk Statistic for information about the Shapiro-Wilk test for normality in PROC UNIVARIATE. 2 Asymmetric Confidence Interval for the Mean 76 3. Minitab Confidence Intervals Minitab • At the top is the Session Window • At the bottom is the Worksheet • Enter the data into a column using cut-and-paste from excel Summary Statistics and Plots Stat ! basic stats ! display descriptive statistics ! enter x as the variable (type or click on x in the box. RandomForestClassifier objects. Chapter 3 R Bootstrap Examples Bret Larget February 19, 2014 Abstract This document shows examples of how to use R to construct bootstrap con dence intervals to accompany Chapter 3 of the Lock 5 textbook. Figure 1-20: Histogram and density curve of bootstrap distribution of difference in sample mean GPAs (male minus female) with observed difference (solid vertical line) and quantiles that delineate the 90% confidence intervals (dashed vertical lines). When computing the interval width, you should round the data up to the next higher whole number to come up with values that are convenient to use. Straight line formula Central to simple linear regression is the formula for a straight line that is most commonly represented as y mx c. I'll cover confidence intervals, prediction intervals, and tolerance intervals. A confidence interval for the population mean gives an indication of how accurately the sample mean estimates the population mean. Chapter 11 Estimating Means with Confidence 1. For SAS coding, you cannot directly specify the confidence level, C, however, you can specify alpha. To illustrate how they work, though, I'm first going to generate a two samples from normal distributions with standard deviations of 1 and means of 0. A confidence interval is an indicator of your measurement's precision. Profile likelihood confidence intervals are based on the log-likelihood function. derivatives. run the analysis. This interval should not be confused with the smaller 95% confidence interval for the mean. The confidence interval for a proportion can be determined with the binom. 2476 minutes. 5 and 0, respectively. 1 is small but not trivial. 1 assume that the heights are normally distributed, so you should check this assumption before using these confidence limits. However, I found that R does the one-sided test slightly differently (infact its…. Assumptions and Conditions \n. If 100 men and 75 women were surveyed, find the 90% confidence interval for the data’s true difference in proportions. Kim Foreword by Kelly S. The definition of histogram differs by source (with country-specific biases). Student’s t distribution is the correct choice for this environment. 01 level (two-tailed). Tell the computer which columns (variables) contain the two samples. However, confidence intervals are not always appropriate. Enter Confidence interval to calculate for the mean, median and standard deviation. This phrasing makes it seem as if the population mean is variable, but it's not. C) Confidence interval will decrease with the introduction of outliers. If it is known that a confidence interval does not correspond to a Bayesian procedure, warn readers that the confidence interval cannot be interpreted as having a X% probability of containing the parameter, that it cannot be interpreted in terms of the precision of measurement,. 95 square units, (95% of the area). We illustrate the use of this command for the lizard tail length data. It’s a powerful technique that is similar to the Jackknife. Get Started. histfit(data,nbins,dist) plots a histogram with nbins bins and fits a density function from the distribution specified by dist. A 100(1-a)% confidence interval for the unknown multinomial population given an observed sample histogram is a polygon on D M-1 containing 100(1-a)% of its Dirichlet posterior. Each column of ci has the endpoints of a conficence interval. Let's have a blitz on calculating confidence intervals using R! An approximate large-sample confidence interval for the population mean If we have a large random sample of size n from a population, we can calculate an approximate confidence interval for the population mean using the following R function:. Presentation of Study Results: Histograms with Confidence Interval Summary Elke Dietrich Stefan GroS Heinz-Otto Keinecke Harald Kwasny - Behringwerke AG - Bebringwerke AG - Behringwerke AG - Bebringwerke AG The SAS® Macro CIRCLES (PC DOS, Release 6. example h = histfit( ___ ) returns a vector of handles h , where h(1) is the handle to the histogram and h(2) is the handle to the density curve. Do not worry about labeling everything appropriately before you create a histogram. For some distributions and sample sizes, the confidence intervals work well. Call this probability π. Confidence Interval Width Calculator. Plot the 50 confidence intervals. The prediction interval seem to be fine, but the confidence interval seems to be wrong. In the example below we will use a 95% confidence level and wish to find the confidence interval. I’ll cover confidence intervals, prediction intervals, and tolerance intervals. The range of the quantitative variable is divided into intervals called bins which are plotted on the x-axis. Tracing it includes an unexpected dip into R's C implementation. As bbum says, it's so "google can organize my head. type: the type of plot to be drawn, default to histogram like vertical lines. I have got the fitted values and the confidence intervals as vectors. For numeric variables, we can summarize data with the center and spread. Confidence Interval for a Sample Mean: A simulation This app randomly samples N data points from a Normal Distribution. Further detail of the predict function for linear regression model can be found in the R documentation. This chart is a variation of a Histogram that uses kernel smoothing to plot values, allowing for smoother distributions by smoothing out the noise. To further assure that there is no bias present, the lab supervisor constructs a 95% confidence interval around the average hardness result. A confidence interval is a interval of plausible values for a population characteristic (e. Finally, the third method makes use of a customized R command. Note that the histogram is quite skewed. However, when. Confidence intervals are almost always constructed to be equal-tailed and we will restrict attention to equal-tailed intervals 111 our discussiOn. For the lower confidence limit, change the label to "Lower Confidence Limit (&[Confidence Level])". population proportion, population mean). up vote 1 down vote favorite. Confidence Interval Width Calculator. Here is an example of Calculating a confidence interval: For the last question we are going to calculate a confidence interval, and then use that to determine if one company is better than the other, and if so, which one is better. Confidence intervals provide a range of model skills and a likelihood that the model skill will fall between the ranges when making predictions on new data. 8 ng/mL with ranges of values obtained for the lower and upper boundaries of 0. Depth effects on wave directionality, measured by R, are represented by R S = β S R by way of the factor β S, and α S is the depth factor. For numeric variables, we can summarize data with the center and spread. I have got the fitted values and the confidence intervals as vectors. It is the range from 47. Bootstrap conﬁdence intervals Class 24, 18. Unselect pool variances (we are using the unequal variances approach, to be safe). Binomial Confidence Intervals. The mediated effect is the product of two regression coefficients. The median in statistics is the middle value of a data set ordered from largest to smallest. Confidence intervals give us a range of plausible values for some unknown value based on results from a sample. Note: 3 is the true mean. for the true mean change in weight Example of graph comparing 95% confidence intervals Age-Gender Group. One problem is the sensitivity of a histogram to the choice of breakpoints for the bars - small changes can alter the visual impression quite drastically in some cases. The lower and upper confidence levels (CLs) show that the best results for normality were reached with Lambda values between -2. The confidence intervals calculated above are when your data is complete and does not contain any missing data. Learn more about confidence intervall, mean, t-distribution, confidence interval. Computes confidence intervals for regression parameters in svyglm objects. Using Stata for Confidence Intervals All of the confidence interval problems we have discussed so far can be solved in Stata via either (a) statistical calculator functions, where you provide Stata with the necessary summary statistics for means, standard deviations, and sample sizes; these commands end with an i, where the i. The "exact" method uses the F distribution to compute exact (based on the binomial cdf) intervals; the "wilson" interval is score-test-based; and the "asymptotic" is the text-book, asymptotic normal interval. I used optim command to obtain the maximum likelihood estimates using some starting values. $\endgroup$ - Ian Nov 4 '15 at 13:56 $\begingroup$ I still don't really understand what exactly you want with the theoretical confidence interval, even after seeing your suggestion as to what it. 01 level (two-tailed). Tutorial for new R users whom need an accessible and easy-to-understand resource on how to create their own histogram with basic R. The following figure displays the histogram from 1000 sample means superimposed with a Gaussian curve. Histogram Intervals If you specify the MIDPERCENTS suboption in parentheses after a density estimate option, PROC UNIVARIATE includes a table that lists the interval midpoints along with the observed and estimated percentages of the observations that lie in the interval. This tutorial aimed at explaining what histograms are and how they differ from bar charts. Suppose we want to estimate and obtain the confidence interval estimate of the average GPA ($\mu$) of all undergraduate students at University of Notre Dame. This topic covers confidence intervals for means and proportions. Table with modified confidence interval label. They consider the sample size and the potential variation in the population and give us an estimate of the range in which the real answer lies. The graph on the left shows the means and 95% confidence interval for the mean in each of the four groups. Confidence interval you want (usually 95%): Decimal places you want for the answers: Notes; This program runs within R (www. PY - 2011/9. ” Then if we. 96, we would have a “95% confidence interval. Simulating Confidence Intervals. Choose Your Course of Study. percentile() and passing in two arguments: The array bs_replicates, and the list of percentiles - in this case 2. There are two common ways to construct a comparative histogram: you can create a panel of histograms, or you can overlay histograms in a single graph. 9 were chosen so as to capture that 95%. A histogram consists of parallel vertical bars that graphically shows the frequency distribution of a quantitative variable. R calculates a 95% confidence interval by default, but we can request other confidence levels using the 'conf. However, I would expect much closer to 900 of the confidence intervals for a normal distrubution at a 90% confidence. This tutorial aimed at explaining what histograms are and how they differ from bar charts. This is a quick tutorial on how to make a 95% confidence interval in R using the normal distribution. A number or string that indicates which column of object contains the parameter estimates to use for the confidence interval or hypothesis test. and Keeping, E. Further detail of the predict function for linear regression model can be found in the R documentation. How does R calculate histogram break points? Thursday December 25, 2014. To illustrate how they work, though, I'm first going to generate a two samples from normal distributions with standard deviations of 1 and means of 0. Confidence intervals are calculated for the mean and median. To change the number of intervals: Double click on the histogram (make sure that you are clicking the whole graph (i. • The distribution of the process considered is Normal. A 100(1-a)% confidence interval for the unknown multinomial population given an observed sample histogram is a polygon on D M-1 containing 100(1-a)% of its Dirichlet posterior. Draw confidence interval on histogram with ggplot2. Histograms with Equal Intervals. A histogram is a plot with rectangles, height of which represents the frequency or “count” of the occurrence and width is equal to the grouping interval. Part 1 – The Sampling Distribution of the Mean How does behave? The surprising thing is that it behaves normally with the same center as. This range means that the 95% confidence interval of rents in Manhattan, based on 1,000 bootstrapped samples from our original 20-apartment sample, is ($2,160, $2,875). R doesn't always give you the value you set. Solution: (B) We know that confidence interval depends on the standard deviation of the data. The variance of the population is assumed to be unknown. Bootstrap Confidence Intervals. The histogram shows the distribution which the subsampling bootstrap uses to calculate critical values. predicted and residuals vs. The real goal of confidence intervals is to determine whether a measure (be it sample mean or individual measure) is statistically different from the population mean. Comparisong of confidence interval for small and large samples. Here is a quick summary of what the different terms mean. Certain assumptions were required in order to be able to determine a confidence interval for a mean. The median in statistics is the middle value of a data set ordered from largest to smallest. 7 shows a 99% confidence interval around a sample mean of 50. , a squared multiple correlation), given the value of the R-square, the number of predictors in the model, and the total sample size. I would actually say that you're probably fine. How to calculate the confidence interval. Example 1 : Create a chart of the 95% confidence and prediction intervals for Example 1 of the Confidence and Prediction Intervals (whose data is duplicated in columns A and B of. For greater control, use ggplot() and other functions provided by the package. Also explore the widths of confidence intervals for different confidence levels. Confidence vs. The confidence interval is estimated using a bootstrap; for large datasets, it may be advisable to avoid that computation by setting this parameter to None. For the purchase amounts in Exercise. You could also add linear regression lines and confidence intervals to the scatterplot. The level should be entered as a percentage, between 50 and 100, without the % sign. ) The second two columns are the 95% prediction intervals. This blog started as a record of my adventures learning bioinformatics and using Python. pvaf function. What we really ought to do is to adjust this "rough" multiplier so that the interval will have the level of confidence that it advertises. You will also learn how to display the confidence intervals and the prediction intervals. Tutorial for new R users whom need an accessible and easy-to-understand resource on how to create their own histogram with basic R. On a bar chart, the height of the bar gives the frequency. The true limits of any interval extend one half unit beyond the endpoints established for the interval and are represented in this manner on the horizontal axis. Thus the height of a rectangle is proportional to the number of points falling into the cell, as is the area provided the breaks are equally-spaced. Here I present ways to customize your histogram for your needs. Following Agresti and Coull, the Wilson interval is to be preferred and so is the default. Multiplicity adjustments. The code below plots of the sampling distribution of the mean and calculate 20 lots of 95% confidence intervals. Sample means are computed for each simulated sample. 95 square units, (95% of the area). RandomForestRegressor and sklearn. test but I don't know how to store the values for the CI to use them in abline. Confidence intervals are about risk. It is built for making profressional looking, plots quickly with minimal code. The new interval takes place during the same timespan as the original interval, but has the opposite direction. Bootstrapping in R - A Tutorial confidence intervals of their respective R- •View histograms (frequency and density). Another instance refers to the information collected by national statistical institutes in relation to income and population dynamics. Student’s t distribution is the correct choice for this environment. 04) produces a graph which allows both. Their 95% confidence intervals are larger, though, basically because there are more possibilities - and, because there are so many of them ( f2 or 36), you would expect at least one or two digram bars to be out of its 95% confidence interval for every test of a perfectly fair six-sided die. 95 square units, (95% of the area). Add a horizontal line showing the location of the true mean. With the right software (such as SPSS), you can create and inspect histograms very fast and doing so is an excellent way for getting to know your data. First, load ggvis and dplyr: library(ggvis) library(dplyr). Compute the 95% confidence interval using np. 5 and 0, respectively. I would like to know how to plot the 68% confidence interval of a Gaussian in such a way the resulting image looks similar to this one: I am trying to do that with R, but I want to resort to Matlab again for my research and I want to see its abilities for statistics. In the graph below each horizontal line represents a confidence interval surrounding a study. Finding confidence intervals for two populations can be broken down to an easy three steps. Thus the height of a rectangle is proportional to the number of points falling into the cell, as is the area provided the breaks are equally-spaced. using ggpubr [code]library(ggpubr) ggerrorplot(DF, x = "division", y = "DeathRate", desc_stat = "mean_ci", color = ". Because I often get this question from people unfamiliar with R, it assumes no prior knowledge of R. To change the number of intervals: Double click on the histogram (make sure that you are clicking the whole graph (i. A relative frequency histogram uses the same information as a frequency histogram but compares each class interval to the total number of items. Building a simple model to test To demonstrate how to get an AUC confidence interval, let’s build a model using a movies dataset from Kaggle ( you can get the data here ). 4 we need to find the multiplier t* values for 24 degrees of freedom and 95% or 99% confidence. Sample means are computed for each simulated sample. A) Confidence interval is robust to outliers.