A graph of a normal bell curve showing statistics used in standardized testing assessment. The scales include standard deviations, cumulative percentages, percentile *****alents, Z-scores, T-scores, standard nines, and percentages in standard nines.
Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities. Statistics are also used for making informed decisions.
Statistical methods can be used to summarize or describe a collection of data; this is called de******ive statistics. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and then used to draw inferences about the process or population being studied; this is called inferential statistics. Both de******ive and inferential statistics comprise applied statistics. There is also a discipline called mathematical statistics, which is concerned with the theoretical basis of the subject.
The idea that statistics branched off from mathematics is a widely held misconception. Some place an undue emphasis on the relationship, but the two disciplines are very different.
The word statistics is also the plural of statistic (singular), which refers to the result of applying a statistical algorithm to a set of data, as in economic statistics, crime statistics, etc. Statistics encompasses the collection, analysis and interpretation of data. Statistics originated as a study of the techniques for collecting and interpreting information. The term was first used in the middle of the 18th century by Gottfried Achenwall, a professor at Gottingen, to explain the physical, moral, and political condition of states.
It is ultimately derived from the New Latin term statisticum collegium ("council of state") and the Italian word statista ("statesman" or "politician"). The GermanStatistik, first introduced by Gottfried Achenwall (1749), originally designated the analysis of data about the state, signifying the "science of state" (then called political arithmetic in English). It acquired the meaning of the collection and classification of data generally in the early 19th century. It was introduced into English by Sir John Sinclair.
Thus, the original principal purpose of Statistik was data to be used by governmental and (often centralized) administrative bodies. The collection of data about states and localities continues, largely through national and international statistical services. In particular, censuses provide regular information about the population.
During the 20th century, the creation of precise instruments for public health concerns (epidemiology, biostatistics, etc.) and economic and social purposes (unemployment rate, econometry, etc.) necessitated substantial advances in statistical practices: the Western welfare states developed after World War I had to possess specific knowledge of the "population".
Today the use of statistics has broadened far beyond its origins as a service to a state or government. Individuals and organizations use statistics to understand data and make informed decisions throughout the natural and social sciences, medicine, business, and other areas.
Statistics is generally regarded not as a subfield of mathematics but rather as a distinct, albeit allied, field. Many universities maintain separate mathematics and statistics departments. Statistics is also taught in departments as diverse as psychology, education, and public health.
In applying statistics to a scientific, industrial, or societal problem, one begins with a process or population to be studied. This might be a population of people in a country, of crystal grains in a rock, or of goods manufactured by a particular factory during a given period. It may instead be a process observed at various times; data collected about this kind of "population" constitute what is called a time series.
For practical reasons, rather than compiling data about an entire population, one usually studies a chosen subset of the population, called a sample. Data are collected about the sample in an observational or experimental setting. The data are then subjected to statistical analysis, which serves two related purposes: de******ion and inference.
De******ive statistics can be used to summarize the data, either numerically or graphically, to describe the sample. Basic examples of numerical de******ors include the mean and standard deviation. Graphical summarizations include various kinds of charts and graphs.
The concept of correlation is particularly noteworthy. Statistical analysis of a data set may reveal that two variables (that is, two properties of the population under consideration) tend to vary together, as if they are connected. For example, a study of annual income and age of death among people might find that poor people tend to have shorter lives than affluent people. The two variables are said to be correlated. However, one cannot immediately infer the existence of a causal relationship between the two variables; see correlation does not imply causation. The correlated phenomena could be caused by a third, previously unconsidered phenomenon, called a lurking variable.
If the sample is representative of the population, then inferences and conclusions made from the sample can be extended to the population as a whole. A major problem lies in determining the extent to which the chosen sample is representative. Statistics offers methods to estimate and correct for randomness in the sample and in the data collection procedure, as well as methods for designing robust experiments in the first place; see experimental design.
The fundamental mathematical concept employed in understanding such randomness is probability. Mathematical statistics (also called statistical theory) is the branch of applied mathematics that uses probability theory and analysis to examine the theoretical basis of statistics.
The use of any statistical method is valid only when the system or population under consideration satisfies the basic mathematical assumptions of the method. Misuse of statistics can produce subtle but serious errors in de******ion and interpretation — subtle in that even experienced professionals sometimes make such errors, and serious in that they may affect social policy, medical practice and the reliability of structures such as bridges and nuclear power plants. Even when statistics is correctly applied, the results can be difficult to interpret for a non-expert. For example, the statistical significance of a trend in the data — which measures the extent to which the trend could be caused by random variation in the sample — may not agree with one's intuitive sense of its significance. The set of basic statistical skills (and skepticism) needed by people to deal with information in their everyday lives is referred to as statistical literacy.
A common goal for a statistical research project is to investigate causality, and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on response or dependent variables. There are two major types of causal statistical studies, experimental studies and observational studies. In both types of studies, the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types is in how the study is actually conducted. Each can be very effective.
An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation may have modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Instead data are gathered and correlations between predictors and the response are investigated.
An example of an experimental study is the famous Hawthorne studies which attempted to test changes to the working environment at the Hawthorne plant of the Western Electric Company. The researchers were interested in whether increased illumination would increase the productivity of the assembly line workers. The researchers first measured productivity in the plant then modified the illumination in an area of the plant to see if changes in illumination would affect productivity. As it turns out, productivity improved under all the experimental conditions (see Hawthorne effect). However, the study is today heavily criticized for errors in experimental procedures, specifically the lack of a control group and blindedness.
An example of an observational study is a study which explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then perform statistical analysis. In this case, the researchers would collect observations of both smokers and non-smokers, perhaps through a case-control study, and then look at the number of cases of lung cancer in each group.
The basic steps for an experiment are to:
plan the research including determining information sources, research subject selection, and ethical considerations for the proposed research and method,
design the experiment concentrating on the system model and the interaction of independent and dependent variables,
See: Stanley Stevens' "Scales of measurement" (1946): nominal, ordinal, interval, ratio
There are four types of measurements or measurement scales used in statistics. The four types or levels of measurement (nominal, ordinal, interval, and ratio) have different degrees of usefulness in statistical research. Ratio measurements, where both a zero value and distances between different measurements are defined, provide the greatest flexibility in statistical methods that can be used for analysing the data. Interval measurements have meaningful distances between measurements but no meaningful zero value (such as IQ measurements or temperature measurements in Fahrenheit). Ordinal measurements have imprecise differences between consecutive values but a meaningful order to those values. Nominal measurements have no meaningful rank order among values.
Variables conforming only to nominal or ordinal measurements are sometimes called together categorical variables since they cannot reasonably be numerically measured whereas ratio and interval measurements are grouped together as quantitative or continuous variables due to their numerical nature.
ويلي تهجم على اهمية الاحصاء في علم النفس والذي هو علم قائم بحد ذاته ويسمى علم النفس الاحصائي تكفيه معلومة واحده بان هذا العالم كشف ربط لكثير من العوامل بلامراض مثل سرطان الرئة والتدخين,السكري والشبكية والخ
وهو قام بدراسة الظواهر والتغيرات النفسية وقام باعطائها تفسيرا ..
بعد كل ذلك نشاهد بعض الناس ويلي ما بيعرفو الحمسة من الطمسه يجادل !!
للمزيد من المعلومات يرجى مراجعة كتب مثيرة بعنوان الاحصاء في علم النفس ..وقراءة علم النفس البيئي لعلماء نفس كبار امثال فرانكشتاين و Freedman
يلي ربطو العوامل النفسية المتغيرة للانسان بلطبيعه وطالعو نتائج لاحصائيات استمرو في عملها سنين ويأتي بأخر زمانهم اشخاص ويطعنون بلنتائج !! ؟
نتائح الاحصاء ما ضروري تجي مطابقة الك وفينك ببساطة ما تأمن فيها بس وقت شخص بيفرغ من وقته سنتين من اجل فكرة معينة ما فينك لا انت ولا مليون واحد متلك انو ينكرها ..انت حر فكر اكيد ما حدا قال لاتشغل مخك واكيد مو قرأن منزل من السماء ..بس هي شرح لشريحة كبيرة منالناس انو هنن هيك اذا ماكنت انت هيك مالك شاذ او خالفت الاوامر ولكن لاتتهجم ..وانت لهلق ما بعرف اذا عندك اطلاع بسيط عن علم الاحصاء فلذلك رح انهي النقاش معك لبين ما تقرأ يلي بعتلك ياه بلروابط ...