Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Sampuli Tabakabaka ya Kulingana na Uwanja× | Sampuli ya Kisistemati× | |
|---|---|---|
| Nyanja | Metodolojia ya Dodoso | Metodolojia ya Dodoso |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1934 (Neyman's stratified sampling theory); field applications throughout 20th century | Mid-20th century (Cochran 1953; Kish 1965) |
| Mwanzilishi≠ | Jerzy Neyman (stratified sampling theory); applied broadly in field survey practice | William G. Cochran; formalized in survey sampling theory |
| Aina | Probability sampling design | Probability sampling design |
| Chanzo asilia | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 |
| Majina mbadala | field stratified sampling, stratified field survey sampling, in-field stratified sampling, field survey stratification | interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | Field-based stratified sampling divides a geographically dispersed or heterogeneous target population into internally homogeneous subgroups (strata) defined by features observable in the field — such as land use type, habitat zone, administrative district, or community category — and then independently draws random samples from each stratum during on-site data collection. The approach combines the precision gains of stratification with the logistical realities of fieldwork, ensuring that every identifiable subgroup of the landscape or community is represented in the final data set. | Systematic sampling is a probability sampling technique in which every k-th element is selected from an ordered list of the population after a random starting point. With population size N and desired sample size n, the sampling interval k = N/n is computed and one unit is chosen at random from the first interval; all subsequent units are selected by adding k repeatedly. The method is operationally simple, yields a spread-out sample, and often achieves lower variance than simple random sampling when the list has no harmful periodicity. |
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