Most studies only sample part of a population, so results don't fully represent the whole population. Any estimates obtained from the sample only approximate the population value. Confidence intervals allow statisticians to express how closely the sample estimate matches the true value in the whole population. Often they are expressed as 95% confidence intervals. Formally, a 95% confidence interval for a value is a range where, if the sampling and analysis were repeated under the same conditions (yielding a different dataset), the interval would include the true (population) value in 95% of all possible cases. This does not imply that the probability that the true value is in the confidence interval is 95%. From the frequentist perspective, such a claim does not even make sense, as the true value is not a random variable. Either the true value is or is not within the given interval. However, it is true that, before any data are sampled and given a plan for how to construct the confidence interval, the probability is 95% that the yet-to-be-calculated interval will cover the true value: at this point, the limits of the interval are yet-to-be-observed random variables. One approach that does yield an interval that can be interpreted as having a given probability of containing the true value is to use a credible interval from Bayesian statistics: this approach depends on a different way of interpreting what is meant by "probability", that is as a Bayesian probability.
In principle confidence intervals can be symmetrical or asymmetrical. An interval can be asymmetrical because it works as lower or upper bound for a parameter (left-sided interval or right sided interval), but it can also be asymmetrical because the two sided interval is built violating symmetry around the estimate. Sometimes the bounds for a confidence interval are reached asymptotically and these are used to approximate the true bounds.
Statistics is applicable to a wide variety of academic disciplines, including natural and social sciences, government, and business. Statistical consultants can help organizations and companies that don't have in-house expertise relevant to their particular questions.
From the 13th to 20th century, there were Swedish-speaking communities in Estonia, particularly on the islands (e.g., Hiiumaa, Vormsi, Ruhnu; in Swedish, known as Dag, Orms, Run, respectively) along the coast of the Baltic, communities which today have all disappeared. The Swedish-speaking minority was represented in parliament, and entitled to use their native language in parliamentary debates. After the loss of Estonia to the Russian Empire in the early 18th century, around 1,000 Estonian Swedish speakers were forced to march to southern Ukraine, where they founded a village, Gammalsvenskby ("Old Swedish Village"). A few elderly people in the village still speak Swedish and observe the holidays of the Swedish calendar, although the dialect is most likely facing extinction.
From 19181940, when Estonia was independent, the small Swedish community was well treated. Municipalities with a Swedish majority, mainly found along the coast, used Swedish as the administrative language and Swedish-Estonian culture saw an upswing. However, most Swedish-speaking people fled to Sweden before the end of World War II, that is, before the invasion of Estonia by the Soviet army in 1944. Only a handful of speakers remain.