Compute a confidence interval around a summary statistic. Only simulation-based methods are (currently only) supported.

Learn more in `vignette("infer")`

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get_confidence_interval( x, level = 0.95, type = "percentile", point_estimate = NULL ) get_ci(x, level = 0.95, type = "percentile", point_estimate = NULL)

x | Data frame of calculated statistics or containing attributes of
theoretical distribution values. Currently, dependent on statistics being
stored in |
---|---|

level | A numerical value between 0 and 1 giving the confidence level. Default value is 0.95. |

type | A string giving which method should be used for creating the
confidence interval. The default is |

point_estimate | A numeric value or a 1x1 data frame set to |

A 1 x 2 tibble with 'lower_ci' and 'upper_ci' columns. Values correspond to lower and upper bounds of the confidence interval.

`get_ci()`

is an alias of `get_confidence_interval()`

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`conf_int()`

is a deprecated alias of `get_confidence_interval()`

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# find the point estimate---mean number of hours worked per week point_estimate <- gss %>% specify(response = hours) %>% calculate(stat = "mean") %>% dplyr::pull() # starting with the gss dataset gss %>% # ...we're interested in the number of hours worked per week specify(response = hours) %>% # hypothesizing that the mean is 40 hypothesize(null = "point", mu = 40) %>% # generating data points for a null distribution generate(reps = 1000, type = "bootstrap") %>% # finding the null distribution calculate(stat = "mean") %>% get_confidence_interval( point_estimate = point_estimate, # at the 95% confidence level level = 0.95, # using the standard error method type = "se" )#> # A tibble: 1 x 2 #> lower_ci upper_ci #> <dbl> <dbl> #> 1 40.1 42.7