Frequency Counting
Frequency Counting - Related Videos
10:19
Using a Hemacytometer to Count Cells
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2023
Many biomedical experiments require manipulation of a known quantity of cells, in order to achieve accurate, reproducible, and statistically-relevant data. Therefore, learning how to count cells is a particularly essential technique for any successful biomedical scientist. The most common way to count cells is by using a hemacytometer - an instrument that bears two laser-etched grids, which aid in the enumeration of an aliquot cells under a simple light microscope. This data can then be used to...
05:47
Counting and Determining the Viability of Cultured Cells
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Cited by 19 •
2008
Determining the number of cells in culture is important in standardization of culture conditions and in performing accurate quantitation experiments. In this video, we demonstrate how cells are counted using a hemacytometer.
06:37
Counting Human Neural Stem Cells
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Cited by 14 •
2007
Knowledge of the exact number of viable cells is required for many tissue culture manipulations. This protocol describes how to differentiate between live and dead cells and quantify cells using a hemacytometer. Although it describes counting human neural stem/precursor cells (hNSPCs), it can be used for other cell types.
11:25
Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
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Cited by 22 •
2009
The anatomical organization of the primate brain can provide important insights into normal and pathological conditions in humans. Unbiased stereology is a method for accurately and efficiently estimating the total neuron number (or other cell type) in a given reference space1.
01:21
Frequency-dependent Selection
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2026
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...
FAQs
Grouping should match the type of data and the purpose of the summary. Categorical observations can be organized by distinct labels, whereas numerical observations may be listed by distinct values or combined into defined class intervals. This choice determines what each tally represents and affects how readily readers can compare categories, inspect concentration, or recognize the overall distribution.
Raw counts show how many observations fall into each group, but relative frequencies express those counts as proportions of the dataset and percentages make comparisons more immediately readable. Cumulative frequencies add counts progressively across ordered values or intervals. Together, these forms allow a distribution to be examined in absolute terms, standardized terms, and running totals.
Frequency patterns can show which observations are common, which are unusual, and whether values are concentrated or spread across categories or intervals. When displayed in a table or graph, the counts also help readers assess distribution shape. This makes frequency counting useful as an initial descriptive step before interpreting broader statistical patterns or selecting later analyses.
Begin by identifying the distinct values or categories that need to be represented. For numerical data, decide whether distinct values or defined class intervals provide the more useful grouping. Tally each observation into its assigned group, check the resulting counts, and, when comparison is needed, add relative, percentage, or cumulative frequency columns.
Class intervals determine which numerical observations are counted together. Narrower or broader intervals can therefore change the level of detail visible in a frequency table or histogram, while still summarizing the same dataset. Researchers choose defined intervals when listing every distinct value would be less practical, then interpret the resulting distribution at that chosen level of grouping.
Percentages are useful when groups differ in size or when results need to be compared in a common scale. A count states the number of observations in a category or interval, whereas its percentage indicates that group’s share of the dataset. This conversion supports clearer comparisons and can make distributions easier to interpret across summaries.
It provides an organized starting point for descriptive statistics by showing how observations are distributed across values, categories, or intervals. Those summaries can help estimate probabilities from observed proportions and inform later hypothesis testing. Frequency tables and visual displays also give researchers an initial view of common, unusual, or patterned observations before more formal statistical analysis.