biostatistics biostatistics biostatistics (also known as biometry) are the development and application of statistical methods to a wide range of topics in biology. it encompasses the design of biological experiments, the collection and analysis of data from those experiments and the interpretation of the results. history biostatistics and genetics biostatistical modeling forms an important part of numerous modern biological theories. genetics studies, since its beginning, used statistical concepts to understand observed experimental results. some genetics scientists even contributed with statistical advances with the development of methods and tools. gregor mendel started the genetics studies investigating genetics segregation patterns in families of peas and used statistics to explain the collected data. in the early 1900s, after the rediscovery of mendel's mendelian inheritance work, there were gaps in understanding between genetics and evolutionary darwinism. francis galton tried to expand mendel's discoveries with human data and proposed a different model with fractions of the heredity coming from each ancestral composing an infinite series. he called this the theory of "law of ancestral heredity". his ideas were strongly disagreed by william bateson, who followed mendel's conclusions, that genetic inheritance were exclusively from the parents, half from each of them. this led to a vigorous debate between the biometricians, who supported galton's ideas, as raphael weldon, arthur dukinfield darbishire and karl pearson, and mendelians, who supported bateson's (and mendel's) ideas, such as charles davenport and wilhelm johannsen. later, biometricians could not reproduce galton conclusions in different experiments, and mendel's ideas prevailed. by the 1930s, models built on statistical reasoning had helped to resolve these differences and to produce the neo-darwinian modern evolutionary synthesis. solving these differences also allowed to define the concept of population genetics and brought together genetics and evolution. the three leading figures in the establishment of population genetics and this synthesis all relied on statistics and developed its use in biology. ronald fisher worked alongside statistician betty allan developing several basic statistical methods in support of his work studying the crop experiments at rothamsted research, published in fisher's books statistical methods for research workers (1925) and the genetical theory of natural selection (1930), as well as allan's scientific papers. fisher went on to give many contributions to genetics and statistics. some of them include the anova, p-value concepts, fisher's exact test and fisher's equation for population dynamics. he is credited for the sentence "natural selection is a mechanism for generating an exceedingly high degree of improbability". sewall g. wright developed f-statistics and methods of computing them and defined inbreeding coefficient. j. b. s. haldane's book, the causes of evolution, reestablished natural selection as the premier mechanism of evolution by explaining it in terms of the mathematical consequences of mendelian genetics. he also developed the theory of primordial soup. these and other biostatisticians, mathematical biologists, and statistically inclined geneticists helped bring together evolutionary biology and genetics into a consistent, coherent whole that could begin to be quantitatively modeled. in parallel to this overall development, the pioneering work of d'arcy thompson in on growth and form also helped to add quantitative discipline to biological study. despite the fundamental importance and frequent necessity of statistical reasoning, there may nonetheless have been a tendency among biologists to distrust or deprecate results which are not qualitatively apparent. one anecdote describes thomas hunt morgan banning the friden calculator from his department at caltech, saying "well, i am like a guy who is prospecting for gold along the banks of the sacramento river in 1849. with a little intelligence, i can reach down and pick up big nuggets of gold. and as long as i can do that, i'm not going to let any people in my department waste scarce resources in placer mining." research planning any research in life sciences is proposed to answer a scientific question we might have. to answer this question with a high certainty, we need accurate results. the correct definition of the main hypothesis and the research plan will reduce errors while taking a decision in understanding a phenomenon. the research plan might include the research question, the hypothesis to be tested, the experimental design, data collection methods, data analysis perspectives and costs involved. it is essential to carry the study based on the three basic principles of experimental statistics: randomization, replication, and local control. research question the research question will define the objective of a study. the research will be headed by the question, so it needs to be concise, at the same time it is focused on interesting and novel topics that may improve science and knowledge and that field. to define the way to ask the scientific question, an exhaustive literature review might be necessary. so the research can be useful to add value to the scientific community. hypothesis definition once the aim of the study is defined, the possible answers to the research question can be proposed, transforming this question into a hypothesis. the main propose is called null hypothesis (h0) and is usually based on a permanent knowledge about the topic or an obvious occurrence of the phenomena, sustained by a deep literature review. we can say it is the standard expected answer for the data under the situation in test. in general, ho assumes no association between treatments. on the other hand, the alternative hypothesis is the denial of ho. it assumes some degree of association between the treatment and the outcome. although, the hypothesis is sustained by question research and its expected and unexpected answers. as an example, consider groups of similar animals (mice, for example) under two different diet systems. the research question would be: what is the best diet? in this case, h0 would be that there is no difference between the two diets in mice metabolism (h0: μ1 = μ2) and the alternative hypothesis would be that the diets have different effects over animals metabolism (h1: μ1 ≠ μ2). the hypothesis is defined by the researcher, according to his/her interests in answering the main question. besides that, the alternative hypothesis can be more than one hypothesis. it can assume not only differences across observed parameters, but their degree of differences (i.e. higher or shorter). sampling usually, a study aims to understand an effect of a phenomenon over a population. in biology, a population is defined as all the individuals of a given species, in a specific area at a given time. in biostatistics, this concept is extended to a variety of collections possible of study. although, in biostatistics, a population is not only the individuals, but the total of one specific component of their organisms, as the whole genome, or all the sperm cells, for animals, or the total leaf area, for a plant, for example. it is not possible to take the measures from all the elements of a population. because of that, the sampling process is very important for statistical inference. sampling is defined as to randomly get a representative part of the entire population, to make posterior inferences about the population. so, the sample might catch the most variability across a population. the sample size is determined by several things, since the scope of the research to the resources available. in clinical research, the trial type, as inferiority, equivalence, and superiority is a key in determining sample size. experimental design experimental designs sustain those basic principles of experimental statistics. there are three basic experimental designs to randomly allocate treatments in all plots of the experiment. they are completely randomized design, randomized block design, and factorial designs. treatments can be arranged in many ways inside the experiment. in agriculture, the correct experimental design is the root of a good study and the arrangement of treatments within the study is essential because environment largely affects the plots (plants, livestock, microorganisms). these main arrangements can be found in the literature under the names of "lattices", "incomplete blocks", "split plot", "augmented blocks", and many others. all of the designs might include control plots, determined by the researcher, to provide an error estimation during inference. in clinical studies, the samples are usually smaller than in other biological studies, and in most cases, the environment effect can be controlled or measured. it is common to use randomized controlled clinical trials, where results are usually compared with observational study designs such as case–control or cohort. data collection data collection methods must be considered in research planning, because it highly influences the sample size and experimental design. data collection varies according to type of data. for qualitative data, collection can be done with structured questionnaires or by observation, considering presence or intensity of disease, using score criterion to categorize levels of occurrence. for quantitative data, collection is done by measuring numerical information using instruments. in agriculture and biology studies, yield data and its components can be obtained by metric measures. however, pest and disease injuries in plats are obtained by observation, considering score scales for levels of damage. especially, in genetic studies, modern methods for data collection in field and laboratory should be considered, as high-throughput platforms for phenotyping and genotyping. these tools allow bigger experiments, while turn possible evaluate many plots in lower time than a human-based only method for data collection. finally, all data collected of interest must be stored in an organized data frame for further analysis. analysis and data interpretation descriptive tools data can be represented through tables or graphical representation, such as line charts, bar charts, histograms, scatter plot. also, measures of central tendency and variability can be very useful to describe an overview of the data. follow some examples: frequency tables one type of tables are the frequency table, which consists of data arranged in rows and columns, where the frequency is the number of occurrences or repetitions of data. frequency can be: absolute: represents the number of times that a determined value appear; n = f 1 + f 2 + f 3 + . . . + f n +f_+f_+...+f_ relative: obtained by the division of the absolute frequency by the total number; n i = f i n = in the next example, we have the number of genes in ten operons of the same organism. genes = genes number absolute frequency relative frequency 1 0 0 2 1 0.1 3 6 0.6 4 2 0.2 5 1 0.1 line graph figure a: line graph example. the birth rate in brazil (2010–2016); figure b: bar chart example. the birth rate in brazil for the december months from 2010 to 2016; figure c: example of box plot: number of glycines in the proteome of eight different organisms (a-h); figure d: example of a scatter plot. line graphs represent the variation of a value over another metric, such as time. in general, values are represented in the vertical axis, while the time variation is represented in the horizontal axis. bar chart a bar chart is a graph that shows categorical data as bars presenting heights (vertical bar) or widths (horizontal bar) proportional to represent values. bar charts provide an image that could also be represented in a tabular format. in the bar chart example, we have the birth rate in brazil for the december months from 2010 to 2016. the sharp fall in december 2016 reflects the outbreak of zika virus in the birth rate in brazil. histograms example of a histogram.the histogram (or frequency distribution) is a graphical representation of a dataset tabulated and divided into uniform or non-uniform classes. it was first introduced by karl pearson. scatter plot a scatter plot is a mathematical diagram that uses cartesian coordinates to display values of a dataset. a scatter plot shows the data as a set of points, each one presenting the value of one variable determining the position on the horizontal axis and another variable on the vertical axis. they are also called scatter graph, scatter chart, scattergram, or scatter diagram. mean the arithmetic mean is the sum of a collection of values ( x 1 + x 2 + x 3 + ⋯ + x n +x_+x_+\cdots +x_ ) divided by the number of items of this collection ( n ). x ¯ = 1 n ( ∑ i = 1 n x i ) = x 1 + x 2 + ⋯ + x n n =\left(\sum _^\right)=+x_+\cdots +x_ median the median is the value in