UAS MK Statistics for Education
What Is Statistics?
Statistics is a branch of applied mathematics that involves the collection, description, analysis, and inference of conclusions from quantitative data. The mathematical theories behind statistics rely heavily on differential and integral calculus, linear algebra, and probability theory.
People who do statistics are referred to as statisticians. They’re particularly concerned with determining how to draw reliable conclusions about large groups and general events from the behavior and other observable characteristics of small samples. These small samples represent a portion of the large group or a limited number of instances of a general phenomenon.
KEY TAKEAWAYS
- Statistics is the study and manipulation of data, including ways to gather, review, analyze, and draw conclusions from data.
- The two major areas of statistics are descriptive and inferential statistics.
- Statistics can be communicated at different levels ranging from non-numerical descriptor (nominal-level) to numerical in reference to a zero-point (ratio-level).
- Several sampling techniques can be used to compile statistical data, including simple random, systematic, stratified, or cluster sampling.
- Statistics are present in almost every department of every company and are an integral part of investing.
Understanding Statistics
Statistics are used in virtually all scientific disciplines, such as the physical and social sciences as well as in business, the humanities, government, and manufacturing. Statistics is fundamentally a branch of applied mathematics that developed from the application of mathematical tools, including calculus and linear algebra, to probability theory.
In practice, statistics is the idea that we can learn about the properties of large sets of objects or events (a population) by studying the characteristics of a smaller number of similar objects or events (a sample). Gathering comprehensive data about an entire population is too costly, difficult, or impossible in many cases, so statistics start with a sample that can be conveniently or affordably observed.
Statisticians measure and gather data about the individuals or elements of a sample, then they analyze this data to generate descriptive statistics. They can then use these observed characteristics of the sample data, which are properly called “statistics,” to make inferences or educated guesses about the unmeasured characteristics of the broader population, known as the parameters.
Descriptive and Inferential Statistics
The two major areas of statistics are known as descriptive statistics, which describes the properties of sample and population data, and inferential statistics, which uses those properties to test hypotheses and draw conclusions. Descriptive statistics include mean (average), variance, skewness, and kurtosis. Inferential statistics include linear regression analysis, analysis of variance (ANOVA), logit/Probit models, and null hypothesis testing.
Descriptive Statistics
Descriptive statistics mostly focus on the central tendency, variability, and distribution of sample data. Central tendency means the estimate of the characteristics, a typical element of a sample or population. It includes descriptive statistics such as mean, median, and mode.
Variability refers to a set of statistics that show how much difference there is among the elements of a sample or population along the characteristics measured. It includes metrics such as range, variance, and standard deviation.
The distribution refers to the overall “shape” of the data, which can be depicted on a chart such as a histogram or a dot plot, and includes properties such as the probability distribution function, skewness, and kurtosis. Descriptive statistics can also describe differences between observed characteristics of the elements of a data set. They can help us understand the collective properties of the elements of a data sample and form the basis for testing hypotheses and making predictions using inferential statistics.
Inferential Statistics
Inferential statistics is a tool that statisticians use to draw conclusions about the characteristics of a population, drawn from the characteristics of a sample, and to determine how certain they can be of the reliability of those conclusions. Based on the sample size and distribution, statisticians can calculate the probability that statistics, which measure the central tendency, variability, distribution, and relationships between characteristics within a data sample, provide an accurate picture of the corresponding parameters of the whole population from which the sample is drawn.
Inferential statistics are used to make generalizations about large groups, such as estimating average demand for a product by surveying a sample of consumers’ buying habits or attempting to predict future events. This might mean projecting the future return of a security or asset class based on returns in a sample period.
Regression analysis is a widely used technique of statistical inference used to determine the strength and nature of the relationship (the correlation) between a dependent variable and one or more explanatory (independent) variables. The output of a regression model is often analyzed for statistical significance, which refers to the claim that a result from findings generated by testing or experimentation is not likely to have occurred randomly or by chance. It’s likely to be attributable to a specific cause elucidated by the data.
Having statistical significance is important for academic disciplines or practitioners that rely heavily on analyzing data and research.
Mean, Median, and Mode
The terms “mean,” “median,” and “mode” fall under the umbrella of central tendency. They describe an element that’s typical in a given sample group. You can find the mean descriptor by adding the numbers in the group and dividing the result by the number of data set observations.
The middle number in the set is the median. Half of all included numbers are higher than the median, and half are lesser. The median home value in a neighborhood would be $350,000 if five homes were located there and valued at $500,000, $400,000, $350,000, $325,000, and $300,000. Two values are higher, and two are lower.
Mode identifies the number that falls between the highest and lowest values. It appears most frequently in the data set.
Understanding Statistical Data
The root of statistics is driven by variables. A variable is a data set that can be counted that marks a characteristic or attribute of an item. For example, a car can have variables such as make, model, year, mileage, color, or condition. By combining the variables across a set of data, such as the colors of all cars in a given parking lot, statistics allows us to better understand trends and outcomes.
There are two main types of variables:
First, qualitative variables are specific attributes that are often non-numeric. Many of the examples given in the car example are qualitative. Other examples of qualitative variables in statistics are gender, eye color, or city of birth. Qualitative data is most often used to determine what percentage of an outcome occurs for any given qualitative variable. Qualitative analysis often does not rely on numbers. For example, trying to determine what percentage of women own a business analyzes qualitative data.
The second type of variable in statistics is quantitative variables. Quantitative variables are studied numerically and only have weight when they’re about a non-numerical descriptor. Similar to quantitative analysis, this information is rooted in numbers. In the car example above, the mileage driven is a quantitative variable, but the number 60,000 holds no value unless it is understood that is the total number of miles driven.
Quantitative variables can be further broken into two categories. First, discrete variables have limitations in statistics and infer that there are gaps between potential discrete variable values. The number of points scored in a football game is a discrete variable because:
- There can be no decimals.
- It is impossible for a team to score only one point.
Statistics also makes use of continuous quantitative variables. These values run along a scale. Discrete values have limitations, but continuous variables are often measured into decimals. Any value within possible limits can be obtained when measuring the height of the football players, and the heights can be measured down to 1/16th of an inch, if not further.
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Statistical Levels of Measurement
There are several resulting levels of measurement after analyzing variables and outcomes. Statistics can quantify outcomes in four ways.
Nominal-level Measurement
There’s no numerical or quantitative value, and qualities are not ranked. Nominal-level measurements are instead simply labels or categories assigned to other variables. It’s easiest to think of nominal-level measurements as non-numerical facts about a variable.
Example: The name of the president elected in 2020 was Joseph Robinette Biden Jr.
Ordinal-level Measurement
Outcomes can be arranged in an order, but all data values have the same value or weight. Although they’re numerical, ordinal-level measurements can’t be subtracted against each other in statistics because only the position of the data point matters. Ordinal levels are often incorporated into nonparametric statistics and compared against the total variable group.
Interval-level Measurement
Outcomes can be arranged in order, but differences between data values may now have meaning. Two data points are often used to compare the passing of time or changing conditions within a data set. There is often no “starting point” for the range of data values, and calendar dates or temperatures may not have a meaningful intrinsic zero value.
Ratio-level Measurement
Outcomes can be arranged in order, and differences between data values now have meaning. But there’s a starting point or “zero value” that can be used to further provide value to a statistical value. The ratio between data values has meaning, including its distance away from zero.
Statistics Sampling Techniques
It would often not be possible to gather data from every data point within a population to gather statistical information. Statistics relies instead on different sampling techniques to create a representative subset of the population that’s easier to analyze. In statistics, there are several primary types of sampling.
Simple Random Sampling
Simple random sampling calls for every member within the population to have an equal chance of being selected for analysis. The entire population is used as the basis for sampling, and any random generator based on chance can select the sample items. For example, 100 individuals are lined up and 10 are chosen at random.
Systemic Sampling
Systematic sampling calls for a random sample as well, but its technique is slightly modified to make it easier to conduct. A single random number is generated, and individuals are then selected at a specified regular interval until the sample size is complete. For example, 100 individuals are lined up and numbered. The seventh individual is selected for the sample, followed by every subsequent ninth individual, until 10 sample items have been selected.
Stratified Sampling
Stratified sampling calls for more control over your sample. The population is divided into subgroups based on similar characteristics. Then you calculate how many people from each subgroup would represent the entire population. For example, 100 individuals are grouped by gender and race. Then a sample from each subgroup is taken in proportion to how representative that subgroup is of the population.
Cluster Sampling
Cluster sampling calls for subgroups as well, but each subgroup should be representative of the population. The entire subgroup is randomly selected instead of randomly selecting individuals within a subgroup.
Uses of Statistics
Statistics is prominent in finance, investing, business, and in the world. Much of the information you see and the data you’re given is derived from statistics, which are used in all facets of a business.
- Statistics in investing include average trading volume, 52-week low, 52-week high, beta, and correlation between asset classes or securities.
- Statistics in economics include gross domestic product (GDP), unemployment, consumer pricing, inflation, and other economic growth metrics.
- Statistics in marketing include conversion rates, click-through rates, search quantities, and social media metrics.
- Statistics in accounting include liquidity, solvency, and profitability metrics across time.
- Statistics in information technology include bandwidth, network capabilities, and hardware logistics.
- Statistics in human resources include employee turnover, employee satisfaction, and average compensation relative to the market.
Why Is Statistics Important?
Statistics provide the information to educate how things work. They’re used to conduct research, evaluate outcomes, develop critical thinking, and make informed decisions. Statistics can be used to inquire about almost any field of study to investigate why things happen, when they occur, and whether reoccurrence is predictable.
What’s the Difference Between Descriptive and Inferential Statistics?
Descriptive statistics are used to describe or summarize the characteristics of a sample or data set, such as a variable’s mean, standard deviation, or frequency. Inferential statistics employ any number of techniques to relate variables in a data set to one another. An example would be using correlation or regression analysis. These can then be used to estimate forecasts or infer causality.
Who Uses Statistics?
Statistics are used widely across an array of applications and professions. Statistics are done whenever data are collected and analyzed. This can range from government agencies to academic research to analyzing investments.
How Are Statistics Used in Economics and Finance?
Economists collect and look at all sorts of data ranging from consumer spending to housing starts to inflation to GDP growth. In finance, analysts and investors collect data about companies, industries, sentiment, and market data on price and volume. The use of inferential statistics in these fields is known as econometrics. Several important financial models, from the capital asset pricing model (CAPM) to modern portfolio theory (MPT) and the Black-Scholes options pricing model, rely on statistical inference.
The Bottom Line
Statistics is the practice of analyzing pieces of information that might seem conflicting or unrelated at first glance and on the surface. It can lead to a solid career as a statistician, but it can also be a handy metric in everyday life—perhaps when you’re analyzing the odds that your favorite team will win the Super Bowl before you place a bet, gauging the viability of an investment, or determining whether you’re being comparatively overcharged for a product or service.
Sources: https://www.investopedia.com/terms/s/statistics.asp
Telkom University – Statistik dan statistika seringkali menjadi dua konsep yang disamakan, padahal keduanya memiliki perbedaan mendasar dalam konteks penggunaannya. TelUtizen, pemahaman yang tepat mengenai perbedaan ini menjadi kunci dalam penerapan pengetahuan di berbagai disiplin ilmu, mulai dari teknologi informasi hingga bisnis. Artikel ini akan membahas perbedaan statistik dan statistika, serta menyoroti manfaatnya beserta contoh aplikasinya dalam berbagai bidang.
Pengertian Statistik dan Statistika
Statistika (statistics) merupakan bidang ilmu yang memiliki cakupan yang sangat luas. KBBI menjelaskan bahwa statistika adalah ilmu tentang cara mengumpulkan, menabulasi, menggolong-golongkan, menganalisis, dan mencari keterangan yang berarti dari data yang berupa angka. Istilah ini digunakan ketika seseorang ingin menjelaskan ilmu yang berkaitan dengan data berbentuk angka.
Di sisi lain, statistik (statistic) memiliki cakupan yang lebih sempit. Menurut KBBI, statistik merupakan data berupa angka yang dikumpulkan, ditabulasi, digolong-golongkan sehingga dapat memberi informasi yang berarti mengenai suatu masalah atau gejala.
Perbedaan Utama Statistik dan Statistika
Untuk memahami beda statistik dan statistika serta menghindari kekeliruan dalam penggunaannya, terdapat tiga poin utama yang dapat diperhatikan:
Kategori Keilmuan
Statistika: Merujuk pada bidang ilmu secara keseluruhan. Ketika kita belajar statistika, maka kita sedang memahami ilmu yang terlibat dalam pengelompokan, pencatatan, dan analisis angka untuk mencapai suatu kesimpulan.
Statistik: Merujuk pada angka-angka yang menjadi sumber data dan rujukan untuk mencapai kesimpulan tersebut. Ketika berbicara tentang statistik, kita membahas data numerik yang digunakan dalam konteks analisis.
Kategori Kajian
Statistika: Sebagai bidang ilmu, terbagi dalam metode khusus seperti statistika deskriptif dan inferensi, yang dibedakan berdasarkan tujuan akhir pengolahan data.
Statistik: Dibagi berdasarkan klasifikasi bidangnya, misalnya statistik penduduk, harga, pemasaran, demografi, dan lainnya. Pembagian ini memperjelas konteks dan bidang khusus yang menjadi fokus pengumpulan data.
Kategori Tujuan
Statistika: Bertujuan untuk memperoleh gambaran tentang suatu fenomena, permasalahan, atau pendapat umum. Fokusnya ialah pada pemahaman secara umum terhadap suatu area penelitian.
Statistik: Bertujuan secara lebih spesifik dan terfokus. Statistik digunakan sebagai alat untuk mempermudah proses penelitian atau analisis data karena menyediakan angka-angka yang diperoleh dari berbagai metode, seperti survei dan penghitungan jumlah atau frekuensi.
Metode Statistika
Statistika dipakai untuk beragam keperluan, sehingga tidak mengherankan jika terdapat beberapa metode penelitian yang harus TelUtizen lakukan terkait statistik. Berikut beberapa contoh metode statistika yang umum digunakan:
- Analisis Diskriminan: Analisis diskriminan merupakan metode statistika untuk mengelompokkan peristiwa, fenomena, atau individu ke dalam kelompok, kelas, atau klasifikasi tertentu. Metode ini juga digunakan untuk melihat kaitan dalam perbandingan variabel antara individu atau kelompok kecil.
- Analisis Deskriptif: Sesuai dengan namanya, metode statistika ini bertujuan untuk menjelaskan nilai-nilai yang merupakan hasil dari pengumpulan data. Contohnya termasuk nilai rata-rata, nilai median, nilai maksimal dan minimal, dan sebagainya. Metode ini digunakan jika kamu hanya ingin mendeskripsikan hasil penelitian tanpa melakukan analisis terlalu mendetail.
- Analisis Regresi: Metode ini menggunakan variabel yang ada pada masa kini untuk memprediksi pola atau tren di masa depan. Kamu dapat menggunakan satu variabel terikat untuk membandingkannya dengan variabel bebas guna menentukan prediksi tren.
Manfaat Statistik dan Statistika
Statistika memiliki peran yang sangat penting dalam kehidupan sehari-hari. Penggunaan statistika tidak hanya terbatas pada penelitian akademis, tetapi juga memiliki dampak langsung dalam pemecahan masalah di berbagai sektor kehidupan. Berikut adalah beberapa contoh manfaat statistika dalam kehidupan sehari-hari:
- Pendidikan: Statistika digunakan dalam bidang pendidikan untuk mengukur prestasi siswa dan efektivitas program pembelajaran. Contohnya, dengan mengumpulkan data statistik tentang hasil ujian siswa, statistikawan dapat menganalisis kinerja siswa secara menyeluruh dan mengidentifikasi area yang memerlukan perbaikan. Ini mendukung pengembangan program pembelajaran yang lebih efektif di lingkungan pendidikan.
- Kesehatan: Statistika digunakan dalam penelitian kesehatan untuk mengidentifikasi faktor risiko yang mempengaruhi kesehatan individu. Contohnya, melalui analisis data kesehatan, statistikawan dapat mengidentifikasi faktor risiko terkait dengan penyakit tertentu, seperti kanker atau penyakit jantung. Hasil analisis ini memberikan kontribusi dalam pendekatan diagnostik dan pengobatan yang lebih efektif oleh para profesional medis.
- Lingkungan: Statistika digunakan dalam bidang lingkungan untuk mengukur dampak aktivitas manusia terhadap lingkungan dan populasi satwa liar. Contohnya, melalui analisis data tentang populasi satwa liar atau kualitas air, statistikawan dapat mengidentifikasi area yang memerlukan perlindungan atau restorasi. Ini membantu organisasi lingkungan dalam mengembangkan program konservasi yang lebih efektif untuk menjaga keseimbangan ekosistem.
- Ekonomi: Statistika digunakan dalam bidang ekonomi untuk mengukur kinerja ekonomi suatu negara atau perusahaan. Data statistik tentang inflasi, produksi industri, atau tingkat pengangguran membantu pemerintah membuat keputusan ekonomi yang tepat dan membimbing perusahaan dalam mengembangkan strategi bisnis yang lebih baik.
Contoh Statistik dan Statistika
Contoh penerapan statistik dan statistika dalam beberapa bidang, antara lain:
Kesehatan
Dalam bidang kesehatan, statistika digunakan untuk menganalisis data kesehatan, seperti meneliti prevalensi penyakit di suatu populasi atau membandingkan hasil dari berbagai jenis pengobatan. Statistik memainkan peran kunci dalam penelitian medis dan membantu dalam mengidentifikasi tren kesehatan, pola penyakit, serta efektivitas intervensi medis.
Sosial
Statistik digunakan untuk mengumpulkan data sosial, termasuk melalui survei pendapat publik. Ini membantu peneliti dan ilmuwan sosial memahami perilaku manusia, menganalisis tren sosial, dan membuat proyeksi berdasarkan pola-pola yang diamati. Analisis statistik juga dapat memberikan wawasan dalam mengatasi masalah sosial atau merancang kebijakan yang lebih efektif.
Bisnis
Statistik digunakan dalam dunia bisnis untuk memprediksi permintaan pasar, mengukur kinerja bisnis, dan melakukan survei pelanggan. Analisis statistik membantu perusahaan membuat keputusan berdasarkan data yang akurat, seperti dalam perencanaan persediaan atau pengembangan strategi pemasaran.
Itulah tadi penjelasan terkait apa yang dimaksud statistik dan statistika serta perbedaan di antara keduanya. Melalui penjelasan ini, TelUtizen dapat semakin memahami kemampuan dalam menganalisis dan menafsirkan data untuk mendukung kebijakan, inovasi, dan pengembangan di berbagai bidang. Sejalan dengan hal tersebut, salah satu dosen Statistik Fakultas Informatika Telkom University, Aniq Rohmawati menjelaskan terkait peran ilmu statistik dalam menunjang kompetensi lulusan Telkom University.
“Dengan mempelajari ilmu statistik, mahasiswa akan mendapat bekal kemampuan terkait bagaimana menginterpretasikan dan menganalisa data/kejadian/informasi yang ada di sekitar. Disamping itu, mahasiswa akan memperoleh pemahaman tentang ukuran-ukuran statistik dan pengambilan keputusan serta kesimpulan dari data berdasarkan uji statistika.” ujar Aniq.
Mari terus mengembangkan pemahaman dan keterampilan dalam mengolah data, agar dapat memberikan kontribusi positif bagi pengembangan ilmu pengetahuan dan teknologi di masa depan.
Penulis: Isnaini Amirotu N | Editor: Daris Maulana | Foto: Public Relations | Content Research : Muhammad Ridha
Sumber: https://telkomuniversity.ac.id/perbedaan-statistik-dan-statistika-manfaat-serta-contohnya/
