# population proportion calculator

The uncertainty in a given random sample (namely that is expected that the proportion estimate, p̂, is a good, but not perfect, approximation for the true proportion p) can be summarized by saying that the estimate p̂ is normally distributed with mean p and variance p(1-p)/n. The null hypothesis is a statement about the population proportion, which corresponds to the assumption of no effect, and the alternative hypothesis is the complementary hypothesis to the null hypothesis. In case you have any suggestion, or if you would like to report a broken solver/calculator, please do not hesitate to contact us. Instructions: This calculator conducts a Z-test for one population proportion (p). If instead, what you want to do is to compare two sample proportions, you can use this z-test for two proportions calculator, which will help you assess whether the two sample proportions differ significantly. Notice that this calculator works for estimating the confidence interval for one population proportion. Other Calculators you can use This calculator uses the following formula for the confidence interval, ci:ci = p ± Zα/2*√(1/n)*p*(1-p)*FPC,where:FPC = (N-n)/(N-1),Zα/2 is the critical value of the Normal distribution at α/2 (e.g. P 1 - P 2 ≥ D: P 1 - P 2 < D: One (left) Tests whether sample one comes from a population with a proportion that is less than sample two's population proportion by a difference of D. The main properties of a one sample z-test for one population proportion are: The null hypothesis is rejected when the z-statistic lies on the rejection region, which is determined by the significance level ($$\alpha$$) and the type of tail (two-tailed, left-tailed or right-tailed). CI for a population proportion is calculated by taking the point estimation and adding or subtracting it to the margin of error. for a confidence level of 95%, α is 0.05 and the critical value is 1.96), p is the sample proportion, n is the sample size and N is the population size. This website uses cookies to improve your experience. The estimation of the desired precision can also be called as the acceptable error in the estimation which is half the width of the desired confidence interval. The confidence interval for proportions is calculated based on the mean and standard deviation of the sample distribution of a proportion. H0: p1 - p2 = 0, where p1 is the proportion from the first population and p2 the proportion from the second. they like your product, they own a car, or they can speak a second language) to within a specified margin of error. The most commonly used level of Confidence is 95%. Type I error occurs when we reject a true null hypothesis, and the Type II error occurs when we fail to reject a false null hypothesis. When you are dealing with two population proportions, what you want is to compute a confidence interval for the difference between two population proportions. The test has two non-overlapping hypotheses, the null and the alternative hypothesis. Sampling Distribution of the Sample Proportion Calculator Instructions: Use this calculator to compute probabilities associated to the sampling distribution of the sample proportion. We'll assume you're ok with this, but you can opt-out if you wish. For an explanation of why the sample estimate is normally distributed, study the Central Limit Theorem. Sample Size Calculator to Estimate Population Proportion Online sample size calculator to estimate population proportion (prevalence) with a specified level of precision. If D = 0, then tests if sample one comes from a population with a proportion greater than sample two's population proportion. Instructions: This calculator conducts a Z-test for one population proportion (p). As defined below, confidence level, confidence interval… This one proportion z test calculator will allow you to compute the critical values are p-values for this one sample proportion test, that will help you decide whether or not the sample data provides enough evidence to reject the null hypothesis. Functions: What They Are and How to Deal with Them, Depending on our knowledge about the "no effect" situation, the z-test can be two-tailed, left-tailed or right-tailed, The main principle of hypothesis testing is that the null hypothesis is rejected if the test statistic obtained is sufficiently unlikely under the assumption that the null hypothesis is true, The sampling distribution used to construct the test statistics is approximately normal, The p-value is the probability of obtaining sample results as extreme or more extreme than the sample results obtained, under the assumption that the null hypothesis is true, In a hypothesis tests there are two types of errors. Normal Probability Calculator for Sampling Distributions, Inverse Cumulative Normal Probability Calculator, Sampling Distribution of the Sample Proportion Calculator, Calculator to Compare Sample Correlations, Confidence Interval for Proportion Calculator. Use this calculator to determine the appropriate sample size for estimating the proportion of your population that possesses a particular property (eg. Please select the null and alternative hypotheses, type the hypothesized population proportion $$p_0$$, the significance level $$\alpha$$, the sample proportion (or number o favorable cases) and the sample size, and the results of the z-test for one proportion will be displayed for you: More about the z-test for one population proportion so you can better interpret the results obtained by this solver: A z-test for one proportion is a hypothesis test that attempts to make a claim about the population proportion (p) for a certain population attribute (proportion of males, proportion of people underage).