# Parameters And Statistics Examples

In statistics, population may refer to people, objects, events, hospital visits, measurements, etc. data quality assessment. So it makes sense to use unbiased estimates of population parameters. 2, a sample is selected. Statistic and parameter are terms used in statistical analyses. A statistic is a characteristic of a sample. However, a parameter can be determined in a very small population where every. they aslo report that the margin of. Population parameters and sample statistic multiple choice questions & answers (MCQs), quiz for online masters degree. Statistics is a study of data: describing properties of data (descriptive statistics) and drawing conclusions about a population based on information in a sample (inferential statistics). R Backman 33,840 views. Parameters & Statistics. dmp schemas=bert,movies exclude=grant,index,statistics. Now imagine you're not into hats. The prior distribution for log k,8 (for persons k D 1,,6) is assumed nor-. Sample statistics estimate unknown popu-lation parameters. In statistics, population may refer to people, objects, events, hospital visits, measurements, etc. The average weight of the buffalo population is a parameter, which means the estimate is the average weight of the sample. 6: THE POWER FUNCTION-b The power function of a hypothesis test is the pro ability of rejecting H. What is the unreliability of the units for a mission duration of 30 hours, starting the mission at age zero?. A bivariate normal distribution with all parameters unknown is in the ﬂve parameter Exponential family. Thus in the sample, 0. Parameters are some aspect of the population that are unknown, but that we want to estimate. 2, a sample is selected. Once again, the experiment is typically to sample $$n$$ objects from a population and record one. A statistical model is a representation of a complex phenomena that generated the data. What is 62%? A parameter or a statistic and why?. An example is that the Psi (x) equals the first derivative of the above rho function when the usual Least Squares estimation is considered. In this way, as shown in Figure 1. In the process of estimating such a parameter, we summarize, or reduce, the information in a sample of size n , X 1 , X 2 , , X n , to a single number, such as the sample mean $$\bar{X}$$. Using size=1000 means that our sample consists of 1000 independently drawn (pseudo) random numbers. 7 barrels per day. For example, if I were to measure the height of 5000 randomly selected individuals, then find the mean of the heights I collected, the resulting value would be a statistic. It assumes that there is an unknown but objectively ﬁxed parameter θ . Student-t, Chi-square, and F-distribution quantiles: Find x such that (for example) P (T expdp bert/bert directory=data_pump_dir dumpfile=schema_exclude. o Each item to be estimated uses one df. It is a parameter because it is states something about the entire population of butterflies. A point estimate is a single, best estimate of a population parameter. Chapter 4 Parameter Estimation Thus far we have concerned ourselves primarily with probability theory: what events may occur with what probabilities, given a model family and choices for the parameters. sample of observations is independent (I) and identically distributed (ID). For example, the normal distribution has only two parameters: location (the average) and scale (the standard deviation). In order to make good use of the CBO, you need to create statistics for the data in the database. The average symbol for a statistic is an x with a line on top of it. This tutorial covers many aspects of regression analysis including: choosing the type of regression analysis to. a subset of. Explanation A population is the set of all the individuals, items, or objects of interest in a particular study is defined as the Population. adults (age 18 and over), 53% said that they were dissatisfied with the quality of education students receive in kindergarten through grade 12. Two commonly confused terms are variable and parameter; here we explain and contrast them. We start with the one parameter regular Exponential family. The average weight of the buffalo population is a parameter, which means the estimate is the average weight of the sample. o In calculating the sample variance, one estimate of a parameter is used, !. Statistics 3858 : Statistical Models, Parameter Space and We may also further restrict in some examples the set of parameters to be not 0 or 1, that is. Population parameters are statistics (e. parameter: Definable, measurable, and constant or variable characteristic, dimension, property, or value, selected from a set of data (or population) because it is considered essential to understanding a situation (or in solving a problem). Using the returned parameter estimates calculate the PDF and CDF associated with the GEV distribution using the extval_gev function. And, a statistic is a measure of a characteristic of a sample, e. The parameter is the average height of all women aged 20 years or older. It is important to understand that the use of statistics is simply a means to an end. This course is concerned with “Mathematical Statistics”, i. The total number of American males would then become our population. For example: We might be interested in learning about $$\mu$$,. Let’s take a glance at this article to get some more details on the two topics. Accuracy describes how close your statistic is to a particular population parameter. A numerical value used as a summary measure for a sample, such as sample mean, is known as a a. a subset of. 6: THE POWER FUNCTION-b The power function of a hypothesis test is the pro ability of rejecting H. Elements of subjective interpretion are always present in this process. Additional parameters determine the form in which the information is presented. ˆ = X¯ X¯ 1. Statisticians use different symbols to represent estimators and population parameters. A population D. parameter synonyms, parameter pronunciation, parameter translation, English dictionary definition of parameter. And the random variable can be either continuous or discrete. Parameters synonyms, Parameters pronunciation, Parameters translation, English dictionary definition of Parameters. But the truth is that parameter estimation is at the heart of all research. Sufficient, Complete, and Ancillary Statistics Basic Theory The Basic Statistical Model. Lenae can use this statistic to infer that approximately 64% of the town is also concerned about the safety of the town's parks. There are many ways or drawing a sample, but only random (probability) samples let you generalize to a larger population. For example, parameter 8 represents the mass of the liver as a fraction of lean body mass; from previous medical studies, the liver is known to be about 3. sample of observations is independent (I) and identically distributed (ID). Since we did not specify the keyword arguments loc and scale, those are set to their default values zero and one. • Random sample (continued) - Random selection for small samples does not guarantee that the sample will be representative of the population. It is a parameter because it is states something about the entire population of butterflies. Population parameters are statistics (e. On the other end, Inferential statistics is used to make the generalisation about the population based on the samples. Examine 'Maximum Annual River Flow Rate' data using graphics and conventional statistics. What is the population parameter of interest, and what is the correct notation for this parameter? I an august 2012 gallup survey of 1,012 randomly selected U. Chapter 9: Distributions: Population, Sample and Sampling Distributions. Hints for Statistics Using a TI-83. they aslo report that the margin of. Taking the commonly used 95% confidence level as an example, if the same population were sampled multiple times, and interval estimates made on each occasion, in approximately 95% of the cases, the true. You can use x, the sample mean, to estimate μ, the population mean. But the truth is that parameter estimation is at the heart of all research. In statistical terms, a hypothesis is a statement about a population parameter and hypothesis testing is simply a test of the statement about the population parameter. You get a lot of numbers – the sample size, average, standard deviation, range, maximum, minimum and a host of other numbers. Statistic: A descriptive measure for a sample. Using size=1000 means that our sample consists of 1000 independently drawn (pseudo) random numbers. 1 What is Statistics? Statistics is a collection of procedures and principles for gaining and processing information in order to make decisions when faced with uncertainty. A statistic describes a sample, while a parameter describes an entire population. As figure 2 shows, different data quality assessment methods tend to be either closer to “measurement” or closer to “standards and user requirements”. We explain Statistics and Parameters with video tutorials and quizzes, using our Many Ways(TM) approach from multiple teachers. For example, the population mean is represented by the Greek letter mu (μ) and the population standard deviation by the Greek letter sigma (σ). So, there is a big difference between descriptive and inferential statistics, i. dmp schemas=bert,movies exclude=grant,index,statistics. Thus in the sample, 0. A statistic describes a sample, while a parameter describes an entire population. skewed right. Sample mean, sample variance and standard deviation, quantiles such as quartiles and percentiles, and order statistics such as maximum and minimum are all belong to the category of statistics of a sample. Scale parameter – determines the scale of measurement for x (magnitude of the x-axis scale) (think of the standard deviation) Shape parameter – defines the pdf shape within a family of shapes Not all distributions have all the parameters. The procedure next displays parameter estimates and some associated statistics (Figure 73. Once again, the experiment is typically to sample $$n$$ objects from a population and record one. Statistics and parameters are quite similar, as they both describe groups, such as “5% of students like to talk about data analysis”. , x-bar or s. A statistic is something that describes a sample (eg sample mean)and is used as an estimator for. On the other end, Inferential statistics is used to make the generalisation about the population based on the samples. For example, in the definition of a function such as y = f(x) = x + 2, x is the formal parameter (the parameter) of the defined function. If the parameter we're trying to estimate is the population mean, then our statistic is going to be the sample mean. 2) The parameter is the mean amount of sodium consumed by children under the age of ten. the functions are inverted to express the parameters as functions of the moments. Fundamental goal in statistics is to make inferences (assertions) about a large population from a sample subset, especially using small sample size, and in turn whether the population parameters represented by the sample statistics reflect the typical individual. Some examples of rare events include extreme floods and snowfalls, high wind speeds, extreme temperatures, large fluctuations in exchange rates, and market crashes. 3% of lean body mass for young adult males, with little variation. For example, MLE is a prerequisite for the chi-square test, the G-square test, Bayesian methods, inference with missing. A sample is a smaller subset that is representative of a larger population. Let’s take a glance at this article to get some more details on the two topics. A parameter is any summary number, like an average or percentage, that describes the entire population. Normal quantile: Find x such that P (X<=x)=p for a given p where X is normal with mu and sigma 7. It is for this reason that nonparametric methods are also referred to as distribution-free methods. R Backman 33,840 views. (noun) An example of parameter is a guideline in which an experiment is to take place. Example: Using the weights of the simple random sample of men, we obtain these sample statistics: n = 40 and mean = 172. The parameter is the average height of all women aged 20 years or older. This is where samples and statistics come into play. Since we did not specify the keyword arguments loc and scale, those are set to their default values zero and one. The following are the major activities of inferential statistics, and this lecture introduces methods for the first activity of using sample data to estimate population parameters. Examine 'Maximum Annual River Flow Rate' data using graphics and conventional statistics. A small sample is generally regarded as one of size n<30. Example: Using the weights of the simple random sample of men, we obtain these sample statistics: n = 40 and mean = 172. Quizlet flashcards, activities and games help you improve your grades. Statistics are to parameters as. In general, capital letters refer to population attributes (i. This is useful only in the case where we know the precise model family and parameter values for the situation of interest. 1 What is Statistics? Statistics is a collection of procedures and principles for gaining and processing information in order to make decisions when faced with uncertainty. This means you can use existing scripts, or write and test your own parameterized scripts that have no knowledge of Octopus, passing Octopus Variables directly to your scripts as parameters. We explain Statistics and Parameters with video tutorials and quizzes, using our Many Ways(TM) approach from multiple teachers. For a variable x, the variable z = x −µ σ is called the standardized version of x or the standardized variable corresponding to the variable x. M-estimator of location parameter is deﬁned as the solution of the equation ∑ n i =1. This leads us to the second kind of distribution, the sample distribu-tion. - X refers to a set of population elements; and x, to a set of sample elements. A statistic is used to estimate a parameter. e population parameters such as mean, standard deviation etc. ) obtained from the whole population, while sample statistics are the same statistics obtained from a sample of the population (i. It chooses the value of θ which maximizes the likelihood of observed data , in other words, making the available data as likely as possible. The distinction between a population together with its parameters and a sample together with its statistics is a fundamental concept in inferential statistics. For example, the population mean is represented by the Greek letter mu (μ) and the population standard deviation by the Greek letter sigma (σ). a measurable characteristic; a constant factor serving as a limit; guidelines: the basic parameters of our foreign policy Not to be confused with: perimeter. In statistics, a confidence interval is an estimated range of likely values for a population parameter, for example 40 ± 2 or 40 ± 5%. com's Sample Size calculator is an online statistics & probability tool to estimate the correct number of samples from the population or right portion of population to be included in the statistical survey or experiments to draw the effective conclusion about the population, by using standard deviation or proportion method. quantitative data;. Recall, a statistical inference aims at learning characteristics of the population from a sample; the population characteristics are parameters and sample characteristics are statistics. Random (Probability) Samples: • Based on probability theory • Allow generalization • Sample statistics can be calculated. Our parameter estimate from the data is ^. What is a Parameter in Statistics: Accuracy. Example: A different tree's growth rate is 30 cm per year, so its function is h. • Random sample (continued) - Random selection for small samples does not guarantee that the sample will be representative of the population. (In this example, the sample statistics are the sample means and the population parameter is the population mean. The average weight of the buffalo population is a parameter, which means the estimate is the average weight of the sample. The power of any test of statistical significance will be affected by four main parameters: the effect size the sample size (N) the alpha significance criterion (α) statistical power, or the chosen or implied beta (β) All four parameters are mathematically related. In statistics vocabulary, we often deal with the terms parameter and statistic, which play a vital role in the determination of the sample size. Out of a random sample of 200 people, 106 say they support the proposition. It is for this reason that nonparametric methods are also referred to as distribution-free methods. Fundamental goal in statistics is to make inferences (assertions) about a large population from a sample subset, especially using small sample size, and in turn whether the population parameters represented by the sample statistics reflect the typical individual. For example, suppose that a new weight loss program claims that participants lose at least 5 pounds by participating in the program. SPSS Library: Understanding and Interpreting Parameter Estimates in Regression and ANOVA This page was adapted from a web page at the SPSS web page. This is an Internet-based probability and statistics E-Book. There are two main methods used in inferential statistics: estimation and hypothesis testing. One of its most common uses is to model one's uncertainty about the probability of success of an experiment. First, the estimates are shown, followed by their standard errors. i ) = 0 for some function Ψ(x) 7. The “population” in statistics includes all members of a defined group that we are studying or collecting information on for data driven decisions. skewed left data the mean is less than the median. M-estimator of location parameter is deﬁned as the solution of the equation ∑ n i =1. The materials, tools and demonstrations presented in this E-Book would be very useful for advanced-placement (AP) statistics educational curriculum. The failure times are: 93, 34, 16, 120, 53 and 75 hours. Parameters are some aspect of the population that are unknown, but that we want to estimate. The parameter is the average height of all women aged 20 years or older. o This leaves n-1 degrees of freedom for estimating the sample variance. 05 significance. Identifying Parameters and Statistics. A parameter is an attribute that refers to the entire population. The “population” in statistics includes all members of a defined group that we are studying or collecting information on for data driven decisions. A parameter is a value that describes some aspect of a population. theoretical. For example, parameter 8 represents the mass of the liver as a fraction of lean body mass; from previous medical studies, the liver is known to be about 3. There are two main methods used in inferential statistics: estimation and hypothesis testing. Statistics is a study of data: describing properties of data (descriptive statistics) and drawing conclusions about a population based on information in a sample (inferential statistics). So, in that scenario we're going to be looking at, our statistic is our sample mean plus or minus z star. Normal quantile: Find x such that P (X<=x)=p for a given p where X is normal with mu and sigma 7. Estimating Parameters and Determining Sample Sizes Lecture (Elementary Statistics Module) In this lecture we begin the study of methods of inferential statistics. Descriptive measures that describe a SAMPLE are called STATISTICS. For example, a poll may seek to estimate the proportion of adult residents of a city that support a proposition to build a new sports stadium. known parameter µ. A critical part of inferential statistics involves determining how far sample statistics are likely to vary from each other and from the population parameter. It assumes that there is an unknown but objectively ﬁxed parameter θ . can use the sample mean or sample quantiles as descriptive statistics, recording some features of the data and saying nothing about a population or a gener-ative process, we could use estimates of a model’s parameters as descriptive summaries. The set of parameters is no longer fixed, and neither is the distribution that we use. Sample specific Parameters. The model is called a linear model because the mean of the response vector Y is linear in the unknown parameter. 1 PROBABILITY AND INFERENCE The area of descriptive statistics is concerned with meaningful and efficient ways of presenting data. The t-test is the small sample analog of the z test which is suitable for large samples. The REG Procedure PROC REG Statement PROC REG < options >; The PROC REG statement is required. The symbols differ when reporting statistics versus parameters. A statistic used to estimate a parameter is called a point estimator or simply an estimator, the actual numerical value obtained by estimator is called an estimate. We thank SPSS for their permission to adapt and distribute this page via our web site. Population parameters are statistics (e. Given a sample of n observations, the sample average is calculated as: n x x n i ∑ i = =1 (2) where xi represents the ith individual observation. Information recorded about a sample of individuals (often patients) comprises measurements such as blood pressure, age, or weight and attributes such as blood group. For a variable x, the variable z = x −µ σ is called the standardized version of x or the standardized variable corresponding to the variable x. The observability of the statistics is a major factor separating the statistics and the parameter. A population often includes individuals who are no longer members, as well as individuals not yet in the population. The sample proportion times one minus the sample proportion over our sample size. Population Parameters and Sample Statistics Practice 1. For example, you can specify a different percentage for the confidence interval, or compute confidence intervals only for selected parameters. com's Sample Size calculator is an online statistics & probability tool to estimate the correct number of samples from the population or right portion of population to be included in the statistical survey or experiments to draw the effective conclusion about the population, by using standard deviation or proportion method. Distribution fitting involves estimating the parameters that define the various distributions. What is 62%? A parameter or a statistic and why?. Improving the accuracy of statistical models can involve estimating:. It is for this reason that nonparametric methods are also referred to as distribution-free methods. A critical part of inferential statistics involves determining how far sample statistics are likely to vary from each other and from the population parameter. Normal quantile: Find x such that P (X<=x)=p for a given p where X is normal with mu and sigma 7. Understanding which means and standard deviations are parameters and which are statistics. A statistic is something that describes a sample (eg sample mean)and is used as an estimator for. Review of Parameters and Statistics We have now learned some statistics that can be used to estimate population parameters. To the right of each Greek symbol is the symbol for the associated statistic used to estimate it from a sample. Some examples of rare events include extreme floods and snowfalls, high wind speeds, extreme temperatures, large fluctuations in exchange rates, and market crashes. Degrees of freedom are often broadly defined as the number of "observations" (pieces of information) in the data that are free to vary when estimating statistical parameters. M-estimator of location parameter is deﬁned as the solution of the equation ∑ n i =1. In many cases, we can easily determine the minimum sample size needed to estimate a process parameter, such as the population mean. For example, a poll may seek to estimate the proportion of adult residents of a city that support a proposition to build a new sports stadium. Weibull Distribution RRX Example. 9 inches from the sample of 45 women. To find the mean you add up all the numbers in the data set and divide it by the number of numbers there are. Maximum likelihood estimation (MLE) can be applied in most problems, it has a strong intuitive appeal, and often yields a reasonable estimator of µ. 3 lb, which was the weight in the National Transportation and Safety Board’s recommendation. Parameters are some aspect of the population that are unknown, but that we want to estimate. On the way from the “meas-urement” to “standards and user requirements”, information is being more and more con-. Inferential statistics enables you to make an educated guess about a population parameter based on a statistic computed from a sample randomly drawn from that population (see Figure 1). Lenae has found that 64% of the people she surveyed are concerned about the safety of the town's parks. Quiz: Populations, Samples, Parameters, and Statistics. For example, if the, t parameter is the mean µ of a normal distribution hen we write K 1(µ) for the power function, which 0 e m is the probability of rejecting H, given that the tru. ” Population Parameter Sample Statistic 2) How much do gas prices vary in a large city?. Some examples of rare events include extreme floods and snowfalls, high wind speeds, extreme temperatures, large fluctuations in exchange rates, and market crashes. This means you can use existing scripts, or write and test your own parameterized scripts that have no knowledge of Octopus, passing Octopus Variables directly to your scripts as parameters. If statistics are updated for a table or indexed view, you can choose to update all statistics, only index statistics, or only column statistics. Estimating Parameters and Determining Sample Sizes Lecture (Elementary Statistics Module) In this lecture we begin the study of methods of inferential statistics. Here we use minimum distance estimation to nd the generalized method of moments (GMM) estimator which minimizes the loss function of data and parameters :. These two parameters completely define the normal distribution. A part of the population is called a sample. Statistics and Parameters study guide by BabyBre120 includes 5 questions covering vocabulary, terms and more. Statistical Parameters 83 Method 1 When the scores are ordered from lowest to highest and there are an odd number of scores, the middle value will be the median score. Hints for Statistics Using a TI-83. WORKSHEET – Extra examples (Chapter 1: sections 1. For example, if you're estimating p in a Bernoulli process, p is a random variable with a Beta distribution having parameters α and β. And, a statistic is a measure of a characteristic of a sample, e. Create statistics Since Oracle 8i the Cost Based Optimizer (CBO) is the preferred optimizer for Oracle. The search criteria provide flexibility in selecting information you want to retrieve. they aslo report that the margin of. The z-score is only defined if one knows the population parameters; if one only has a sample set, then the analogous computation with sample mean and sample standard deviation yields the Student's t-statistic. Accuracy describes how close your statistic is to a particular population parameter. You enter the data into your software package and run the descriptive statistics. min, max, mean, standard deviation, percentiles, mode, etc. A statistic is a characteristic of a sample. What is the population parameter of interest, and what is the correct notation for this parameter? I an august 2012 gallup survey of 1,012 randomly selected U. De nition: a y% con dence interval (CI) for an unknown population parameter Y is an interval calculated from sample values by a procedure such that if a large number of independent samples is. Consequently, a method of moments estimate for is obtained by replacing the distributional mean µ by the sample mean X¯. The next two columns of the table contain the statistics and the corresponding probabilities for testing the null hypothesis that the parameter is not significantly different from zero. Additional parameters determine the form in which the information is presented. Weibull Distribution RRX Example. The “population” in statistics includes all members of a defined group that we are studying or collecting information on for data driven decisions. skewed left data the mean is less than the median. Descriptive statistics are applied to populations, and the properties of populations, like the mean or standard deviation, are called parameters as they represent the whole population (i. This could be any description such as ‘40% of the students prefer to opt for science’. Parameter estimation plays a critical role in accurately describing system behavior through mathematical models such as statistical probability distribution functions, parametric dynamic models, and data-based Simulink ® models. Inferential statistics use the characteristics in a sample to infer what the unknown parameters are in a given population. Theresa A Scott, MS (Vandy Biostats) Data Analysis 11 / 29 Con dence intervals, cont’d. This feature is not available right now. Parameters are rarely known and are usually estimated by statistics computed in samples. Please try again later. Inferential statistics enables you to make an educated guess about a population parameter based on a statistic computed from a sample randomly drawn from that population (see Figure 1). To Explain: The definitions of Population, sample, Parameter and statistics. They are quite similar as they describe a group. In this lesson the difference between a statistic and a parameter is defined. Understanding which means and standard deviations are parameters and which are statistics. A statistic is something that describes a sample (eg sample mean)and is used as an estimator for. October 2016 – the average price of gasoline in Maryland is \$2. Everything is online and unorganized, so i have difficulty learning because I can't find the right questions to ask because frankly I don't know what the hell is going on. The resulting assigned value is the estimate, or statistic. The following example shows how to use the Wald test to test a simple linear restriction. In statistical terms, a hypothesis is a statement about a population parameter and hypothesis testing is simply a test of the statement about the population parameter. Example Let the parameter space be the set of all -dimensional vectors, i. A statistic T = r(X1,X2,···,Xn) is suﬃcient if and only if the joint density can be factored as follows: f(x1,x2,···,xn|θ) = u(x1,x2,···,xn)v(r(x1,x2,···,xn),θ) (2) where u and v are non-negative functions. In the method of moments approach, we use facts about the relationship between distribution parameters of interest and related statistics that can be estimated from a sample (especially the mean and variance). Consequently, a method of moments estimate for is obtained by replacing the distributional mean µ by the sample mean X¯. This feature is not available right now. Also, descriptive and inferential statistics are not mutually exclusive. Statistical Basics – Parameter Estimation. From a random sample of 50 wells throughout the United States, the official obtains a sample mean of 10. ˆ = X¯ X¯ 1. The average symbol for a statistic is an x with a line on top of it. Parameter implies a summary description of the characteristics of the target population. Out of a random sample of 200 people, 106 say they support the proposition. A population D. I’ve written a number of blog posts about regression analysis and I've collected them here to create a regression tutorial. Chapter 9: Distributions: Population, Sample and Sampling Distributions. Population parameters and sample statistic multiple choice questions & answers (MCQs), quiz for online masters degree. Chapter 4 Parameter Estimation Thus far we have concerned ourselves primarily with probability theory: what events may occur with what probabilities, given a model family and choices for the parameters. Execution Summary SSRS Report - for a user-defined date range, shows report execution statistics such as total reports run, average reports run, number of successful reports, number of failed reports; also shows charts of report executions per day and week; shows top 10 of report users, most executed, longest running and largest reports. skewed left data the mean is less than the median. Statistics is a study of data: describing properties of data (descriptive statistics) and drawing conclusions about a population based on information in a sample (inferential statistics). We start with the one parameter regular Exponential family. - X refers to a set of population elements; and x, to a set of sample elements. Example: Using the weights of the simple random sample of men, we obtain these sample statistics: n = 40 and mean = 172. A population can. , mathematical ideas 1). Examples: The sample mean, is an unbiased estimator of the population mean,. Lenae can use this statistic to infer that approximately 64% of the town is also concerned about the safety of the town's parks. When we look across the responses that we get for our entire sample, we use a statistic. e population parameters such as mean, standard deviation etc. Sample statistics estimate unknown popu-lation parameters. In general, capital letters refer to population attributes (i. The frequentest approach is the classical approach to parameter estimation. In statistics vocabulary, we often deal with the terms parameter and statistic, which play a vital role in the determination of the sample size. You enter the data into your software package and run the descriptive statistics. The failure times are: 93, 34, 16, 120, 53 and 75 hours.