In the 1850s and 1860s, wealthy Americans going to and from Europe introduced the custom of tipping to the United States. Americans brought tipping home with them after supposedly being influenced by the European custom of paying a servant extra for exceptional service.
But the majority of Americans did not embrace the custom of tipping. When questioned about how poor Americans could be compelled to pay even more for their food, diners complained of classism. In the 1860s, the anti-tipping sentiment was so widespread in the United States that it reached Europe.
Despite this early opposition to tipping, it started to catch on in the United States in the years following the Civil War. Employers might avoid paying wages to formerly enslaved people working on railroads or restaurants by asking customers to tip instead.
Though there would be opposition to the practice far into the 1900s, it eventually became more widespread, particularly as restaurant owners understood its financial advantages.
As a result, restaurant servers and waiters are poorly paid, which leads to more of a tipping culture put forth in practice, and one can have a strong uncomfortable stern when customers don’t tip these waiters.
For tipped employees, the federally mandated minimum cash salary is $2.13. This amount is established with the idea that tips will make up the difference between it and the $7.25 federal minimum wage. The Wage and Hour Division of the U.S. Department of Labor discovered over 1,200 infractions of this regulation after looking into 9,000 restaurants. In U.S. and Canada, customers are supposed to tip between 15% to 20% of the entire bill.
Tipping is greatly affected based on discrimination. Service people are not treated equally as humans, and there still exists discrimination on color. It is said that black people service workers are tipped less than white people.
With a very low wage system, tips have become mandatory for service people to be more dependent as a source of income on tips. And this led to mandatory tipping in many service areas.
In countries like Denmark, tipping is less preferred as service workers are paid good wages, and the country never made it mandatory.
There are other countries where tipping is considered a great disrespect towards the service by the workers, and somewhere, tips are never expected, or customers need not pay. Countries like China, Japan, and South Korea don’t accept tips as they are considered rude. Other nations like Malaysia, Vietnam, and New Zealand have started adopting western practices that leave a tip for their good service.
In other countries like France, 15% of the service fee is usually added to the bill, and the service workers appreciate anything other than that. And in Denmark, service charges are included in the bill as per law.
The Sample Size Calculator asks for all the information like Confidence Level, Margin Error, Proportion of Population, and Size of Population. Hence the Calculator calculates and computes the statistical constraints.
The Sample Size calculates the margin of error through the survey. It asks for all the detailsmentioned above and states the Margin Error.
In Statistics, the information could be more precise. It is often inferred by taking a finite population. So the population is usually sampled. Then it is assumed that the other population's characteristics are also assumed with the last details. So it is assumed as there is a group of individuals, which is the proportion, and we take it as "p." The population is distinguishable, so it becomes 1-p in one way. Suppose the proportion of the individuals has brown hair. The other 1-p have black, red, or blond and other color hair. Hence to calculate the p meaning the population. A sample of individuals as "n" should be considered from the same population. Now the sample proportion is represented as p̂. Sadly, one can assume a population with brown hair until the total population is a sample. The calculation p̂ won't equal p because p̂ is stressed due to sampling noise. So basically, it depends on those sampled individuals. Sample statistics can be used to calculate the Confidence Intervals. It indicates the closeness of the calculation of p̂ to the real value of p.
There is often uncertainty in a given sample. It is assumed that the proportion of p̂ is close to p but not perfect. So to summarise, the calculation of p̂ is distributed with the mean p and variance p(1-p)n.
The Central Limit Theorem explains how the sample estimation is distributed. All the confidence levels, intervals, and all sample sizes are calculated. It is distributed concerning sampling distribution.
So the Confidence level is given an interval around p and p̂. Here p̂ is estimated likely to be. It gives a confidence level that is likely to be considered 95%. Hence, the confidence level indicates p̂ it lies in the confidence interval. So one can take 95% of random samples.
So the confidence level is dependent on the size of sample n. The variance of the sample is inversely proportional to n. So the estimation calculation is close to the true proportion and increases the value of n. So there is an acceptable range of error. It can be set and called the margin of error ε. It could be used to solve the sample size as it required for picked confidence intervals to be small than a. Hence this calculation is termed a Sample Calculation.
It is a measurement of uncertainty related to how precise the calculation is concerning the sample. It reflects the population within a studied and chosen Confidence Interval.
Most commonly Chosen Confidence Level range from 90-95%. It has a corresponding z score. It can be calculated with the equation's help, or a popular table can be picked. Remember to use the table as per the available confidence level score. Making use of the Z score sample distribution is normally distributed. It should be stated as a Statistic of a Random Sample. Below is a sample of an experiment related to a survey repeated several times. The confidence level is a percentage of the time. So the resulting interval is found from various repeated tests.
So let's consider these examples
In Stats, the Confidence Level is a calculation of similar values for a parameter. So example
40 ± 2 or 40 ± 5%.
Considering the commonly taken 95% Confidence Level. So if the same population is sampled a couple of times, the interval estimation made every time is approx 95%. The real population parameter can be contained in that interval. The 95% probability is reliable in the further procedure of estimation. It is not completely dependent on the population's first step or param998eter of interest. There is some factor that influences the width of a confidence interval. It includes the sample size, confidence level, and variability of the sample.
There are many equations to calculate a confidence interval. Again, it depends on factors like whether the standard Deviation is big or small. If it is small, then (n<30)/are involved.
The Calculator here provides the following equation for the confidence interval of a proportion. You can use the following equation.
So for Unlimited Population.
CI = p + Zx √p(1-p)/n
CI' = p +z x√p(1-p)/n' X N-n'/N-1.
So let's assume z is the score.
p̂ is for the population proportion.
n and n' are both sample sizes.
N is the size of the population.
For Statistics, a population is a set of events and elements. Because it has some relevance compared to the experiment, it could be about some objects or systems or hypothetical groups. A population is used to refer to a large group of people. So even if there are several people in a certain age group of a specific geographic location. Even, for example, a group of college students in a university.
One important thing is to adjust the equation with a finite population. So the (N-n)/(N-1) term in the finite population is referred to as the population correction. It is crucial because not all sample individuals are independent.
Suppose a library involves ten people in a room with an age level ranging from 1-100, so if the chosen person has an age of 100. So the other individual has a different age or mostly lower age. So the finite population correction accounts for all these factors. Below is an example of a calculation of Confidence Interval with a huge infinite population.
So 120 people are working in a company. Eighty-five people drink coffee. Now find the confidence interval of 99% of the true proportion of coffee drinkers.
CI= p̂± z * sqrt((p(1 - p))/n) .
CI= p̂ ± z√p(1-p)/n
CI = 85/120 ± 2.58 ×√(85/120 * (1 - 85/120)) /120.
CI = 0.70833 ±0.107
= 70.833 \% ±10.71%
The Sample Size is a statistical concept. It involves considering several observations or replications. It is used to calculate the variability. So it should all be included in the Statistical Sample. It is a crucial aspect of an empirical study. It requires reference to a population. More evidently, sample sizes represent parts of a population. It is chosen for any given survey. To calculate, set the margin of error ε and the maximum distance needed to calculate the true Deviation from its original value.
For this, use the above equation of confidence interval. Set the term to the right of ± to the margin error. You can solve the further equation to find the sample size (n).
Below is the equation to calculate the sample size.
For Unlimited Population
While for Finite Population
n'=n/ 1+ z2xp(1-p)/ε2N
Here z is the score.
ε -margin error
Ex-Calculate the sample size to estimate the proportion of people shopping in a Market. Some identify as vegan, so the confidence interval is 95%, and the margin error is 5%. Suppose a population proportion is 0.5% and an unlimited population size. Consider z for 95% confidence level is 1.96. You can refer to the table above in the confidence level for the z score.
n= z ^ 2 ×p̂(1-p̂)/ε^ 2
n = (1.96 ^ 2 * 0.5(1 - 0.5))/(0.05 ^ 2) = 384.16
So for the above equation, 385 people are mandatory. In the above case, some studies calculate the approximate 6 percent population of the US. This population is vegan. So instead of assuming 0.5 for p̂, It is 0.06, and 40 of 500 enter a supermarket on a particular day. Then that day is represented as p̂ is 0.08.
The sample size is used in Market Research. It helps in defining the number of subjects in a survey or experiment. In a large population survey, the sample size is extremely important. Because it's impossible to determine answers roughly, one can take a random sample of an individual and represent it as a whole.
The large samples also mitigate errors. Help in providing more statistically significant results.
This is very simple. One can calculate the tip on the calculator by simple multiplication and addition.
E.g: You visited a restaurant and had food for $500 for which you need to pay a tip of 15 %. How to calculate the percentage here is that
Tip amount= $500 * 0.15
Total amount = bill amount +tips
= $500 +$75
So for a $500 bill, one has to pay a tip of $75, a 15% tip, which makes the total $575 payable.
This simple multiplication and addition have been briefed in this tip calculator below, showing a clear percentage of tip that one can pay with various ranges based on the dinner’s comfort tip percentage.
The Tip Calculator computes the appropriate tip amount for different service fee percentages and displays the tip-inclusive total. In America, it is customary to pay atleast 15% of the tips from the amount spent on service, excluding taxes.
In this calculator, one has to mention the amount spent on the service for which the result shows the tip percentage, tip amount, and the total that has to be paid for the service. Here the tip ranges anything from 5% to 50% with the appropriate amount and total pay.
While going out to eat with your friends and wanting to share the amount among your n friends, the calculation may confuse you. So we have a shared bill calculator that allows you to calculate the payable bill equally among friends.
For which all you need to do is
You will get a result of
How much tip to be paid varies from one business to another. In the U.S., the tip percentage follows as
|1||Bar and restaurants||15-20%|
|3||Room service||Service fee or 15-20%|
|4||Housekeeping||$2 per person per night|
|5||Mechanical services||Few dollars based on bill amount|
|6||Packers and movers||$5 to $20|
|7||House services||$5 to $20|
|8||Salons||$10 to $20|
|9||massage||$10 to $20|
|10||Drivers||$15 to $20|
Tip percentage varies across the continents and with the country’s policies, laws, and culture. Most tourist destinations have started adopting western cultures, so it has now become mandatory to pay them tips for their services. So please research well and get to know how much a person has to tip before leaving the country to visit so that it can save from embarrassment on foreign soil. And the countries that should never be tipped as it is a rude way of responding to the service done.
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